prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What datasets are used?
Context 1: Introduction
Back to 42 BC, the philosopher Cicero has raised the issue that although there were many Oratory classes, there were none for Conversational skills BIBREF0   He highlighted how important they were not only for politics, but also for educational purpose  Among other conversational norms, he claimed that people should be able to know when to talk in a conversation, what to talk depending on the subject of the conversation, and that they should not talk about themselves 
Norms such as these may become social conventions and are not learnt at home or at school  Social conventions are dynamic and may change according to context, culture and language  In online communication, new commonsense practices are evolved faster and accepted as a norm BIBREF1 , BIBREF2   There is not a discipline for that on elementary or high schools and there are few linguistics researchers doing research on this field 
On the other hand, within the Artificial Intelligence area, some Conversational Systems have been created in the past decades since the test proposed by Alan Turing in 1950  The test consists of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from that of a human BIBREF3   Turing proposed that a human evaluator would judge natural language conversations between a human and a machine that is designed to generate human-like responses  Since then, many systems have been created to pass the Turing's test  Some of them have won prizes, some not BIBREF4   Although in this paper we do not focus on creating a solution that is able to build conversational systems that pass the Turing's test, we focus on NDS  From BIBREF5 , "NDS are systems that try to improve usability and user satisfaction by imitating human behavior"  We refer to Conversational Systems as NDS, where the dialogues are expressed as natural language texts, either from artificial intelligent agents (a k a  bots) or from humans 
That said, the current popular name to systems that have the ability to make a conversation with humans using natural language is Chatbot  Chatbots are typically used in conversational systems for various practical purposes, including customer service or information acquisition  Chatbots are becoming more widely used by social media software vendors  For example, Facebook recently announced that it would make Facebook Messenger (its 900-million-user messaging app by 2016), into a full-fledged platform that allows businesses to communicate with users via chatbots  Google is also building a new mobile-messaging service that uses artificial intelligence know-how and chatbot technology  In addition, according to the Wall Street Journal, there are more than 2 billion users of mobile apps  Still, people can be reluctant to install apps  So it is believed that social messaging can be a platform and chatbots may provide a new conversational interface for interacting with online services, as chatbots are easier to build and deploy than apps BIBREF6  
China seems to be the place where chatbots adoption and use is most advanced today  For example, China's popular WeChat messaging platform can take payments, scan QR codes, and integrate chatbot systems  WeChat integrates e-mail, chat, videocalls and sharing of large multimedia files  Users can book flights or hotels using a mixed, multimedia interaction with active bots  WeChat was first released in 2011 by Tecent, a Chinese online-gaming and social-media firm, and today more than 700 million people use it, being one of the most popular messaging apps in the world (The Economist 2016)  WeChat has a mixture of real-live customer service agents and automated replies (Olson 2016) 
Still, current existing chatbot engines do not properly handle a group chat with many users and many chatbots  This makes the chatbots considerably less social, which is a problem since there is a strong demand of having social chatbots that are able to provide different kinds of services, from traveling packages to finance advisors  This happens because there is a lack of methods and tools to design and engineer the coordination and mediation among chatbots and humans, as we present in Sections 2 and 3  In this paper, we refer to conversational systems that are able to interact with one or more people or chatbots in a multi-party chat as MPCS  Altogether, this paper is not meant to advance the state of the art on the norms for MPCS  Instead, the main contributions of this paper are threefold:
We then present some discussion and future work in the last section 
Challenges on Chattering
There are plenty of challenges in conversation contexts, and even bigger ones when people and machines participate in those contexts  Conversation is a specialized form of interaction, which follows social conventions  Social interaction makes it possible to inform, context, create, ratify, refute, and ascribe, among other things, power, class, gender, ethnicity, and culture BIBREF2   Social structures are the norms that emerge from the contact people have with others BIBREF7 , for example, the communicative norms of a negotiation, taking turns in a group, the cultural identity of a person, or power relationships in a work context 
Conventions, norms and patterns from everyday real conversations are applied when designing those systems to result in adoption and match user's expectations  BIBREF8 describes implicit interactions in a framework of interactions between humans and machines  The framework is based on the theory of implicit interactions which posits that people rely on conventions of interaction to communicate queries, offers, responses, and feedback to one another  Conventions and patterns drive our expectations about interactive behaviors  This framework helps designers and developers create interactions that are more socially appropriate  According to the author, we have interfaces which are based on explicit interaction and implicit ones  The explicit are the interactions or interfaces where people rely on explicit input and output, whereas implicit interactions are the ones that occur without user awareness of the computer behavior 
Social practices and actions are essential for a conversation to take place during the turn-by-turn moments of communication  BIBREF9 highlights that a distinguishing feature of ordinary conversation is "the local, moment-by-moment management of the distribution of turns, of their size, and what gets done in them, those things being accomplished in the course of each current speaker's turn " Management of turns and subject change in each course is a situation that occurs in real life conversations based on circumstances (internal and external) to speakers in a dialogue  Nowadays, machines are not prepared to fully understand context and change the course of conversations as humans  Managing dialogues with machines is challenging, which increases even more when more than one conversational agent is part of the same conversation  Some of those challenges in the dialogue flow were addressed by BIBREF10   According to them, we have system-initiative, user-initiative, and mixed-initiative systems 
In the first case, system-initiative systems restrict user options, asking direct questions, such as (Table TABREF5 ): "What is the initial amount of investment?" Doing so, those types of systems are more successful and easier to answer to  On the other hand, user-initiative systems are the ones where users have freedom to ask what they wish  In this context, users may feel uncertain of the capabilities of the system and starting asking questions or requesting information or services which might be quite far from the system domain and understanding capacity, leading to user frustration  There is also a mixed-initiative approach, that is, a goal-oriented dialogue which users and computers participate interactively using a conversational paradigm  Challenges of this last classification are to understand interruptions, human utterances, and unclear sentences that were not always goal-oriented 
The dialog in Table TABREF5 has the system initiative in a question and answer mode, while the one in Table TABREF7 is a natural dialogue system where both the user and the system take the initiative  If we add another user in the chat, then we face other challenges 
In Table TABREF12 , line 4, the user U1 invites another person to the chat and the system does not reply to this utterance, nor to utterances on lines 6, 7 and 8 which are the ones when only the users (wife and husband) should reply to  On the other hand, when the couple agrees on the period and initial value of the investment (line 9), then the system S1 (at the time the only system in the chat) replies indicating that it will invite more systems (chatbots) that are experts on this kind of pair INLINEFORM0 period, initial value INLINEFORM1   They then join the chat and start interacting with each other  At the end, on line 17, the user U2 interacts with U1 and they agree with the certificate option  Then, the chatbot responsible for that, S3, is the only one that replies indicating how to invest 
Table TABREF12 is one example of interactions on which the chatbots require knowledge of when to reply given the context of the dialog  In general, we acknowledge that exist four dimensions of understanding and replying to an utterance in MPCS which a chatbot that interacts in a multi-party chat group should fulfill:
In the next section we present the state of the art and how they fullfil some of these dimensions 
Conversational Systems
In this section we discuss the state of the art on conversational systems in three perspectives: types of interactions, types of architecture, and types of context reasoning  Then we present a table that consolidates and compares all of them 
ELIZA BIBREF11 was one of the first softwares created to understand natural language processing  Joseph Weizenbaum created it at the MIT in 1966 and it is well known for acting like a psychotherapist and it had only to reflect back onto patient's statements  ELIZA was created to tackle five "fundamental technical problems": the identification of critical words, the discovery of a minimal context, the choice of appropriate transformations, the generation of appropriate responses to the transformation or in the absence of critical words, and the provision of an ending capacity for ELIZA scripts 
Right after ELIZA came PARRY, developed by Kenneth Colby, who is psychiatrist at Stanford University in the early 1970s  The program was written using the MLISP language (meta-lisp) on the WAITS operating system running on a DEC PDP-10 and the code is non-portable  Parts of it were written in PDP-10 assembly code and others in MLISP  There may be other parts that require other language translators  PARRY was the first system to pass the Turing test - the psychiatrists were able to make the correct identification only 48 percent of the time, which is the same as a random guessing 
A L I C E  (Artificial Linguistic Internet Computer Entity) BIBREF12 appeared in 1995 but current version utilizes AIML, an XML language designed for creating stimulus-response chat robots BIBREF13   A L I C E  bot has, at present, more than 40,000 categories of knowledge, whereas the original ELIZA had only about 200  The program is unable to pass the Turing test, as even the casual user will often expose its mechanistic aspects in short conversations 
Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter  It passed the 2011 Turing Test at the Technique Techno-Management Festival held by the Indian Institute of Technology Guwahati  Volunteers participate in four-minute typed conversations with either Cleverbot or humans, with Cleverbot voted 59 3 per cent human, while the humans themselves were rated just 63 3 per cent human BIBREF14  
Types of Interactions
Although most part of the research literature focuses on the dialogue of two persons, the reality of everyday life interactions shows a substantial part of multi-user conversations, such as in meetings, classes, family dinners, chats in bars and restaurants, and in almost every collaborative or competitive environment such as hospitals, schools, offices, sports teams, etc  The ability of human beings to organize, manage, and (mostly) make productive such complex interactive structures which are multi-user conversations is nothing less than remarkable  The advent of social media platforms and messaging systems such as WhatsApp in the first 15 years of the 21st century expanded our ability as a society to have asynchronous conversations in text form, from family and friends chatgroups to whole nations conversing in a highly distributed form in social media BIBREF15  
In this context, many technological advances in the early 2010s in natural language processing (spearheaded by the IBM Watson's victory in Jeopardy BIBREF16 ) spurred the availability in the early 2010s of text-based chatbots in websites and apps (notably in China BIBREF17 ) and spoken speech interfaces such as Siri by Apple, Cortana by Microsoft, Alexa by Amazon, and Allo by Google  However, the absolute majority of those chatbot deployments were in contexts of dyadic dialog, that is, a conversation between a single chatbot with a single user  Most of the first toolkits for chatbot design and development of this initial period implicit assume that an utterance from the user is followed by an utterance of the chatbot, which greatly simplifies the management of the conversation as discussed in more details later  Therefore, from the interaction point of view, there are two types: 1) one in which the chatbot was designed to chat with one person or chatbot, and 2) other in which the chatbot can interact with more than two members in the chat 
Dyadic Chatbot
A Dyadic Chatbot is a chatbot that does know when to talk  If it receives an utterance, it will always handle and try to reply to the received utterance  For this chatbot to behave properly, either there are only two members in the chat, and the chatbot is one of them, or there are more, but the chatbot replies only when its name or nickname is mentioned  This means that a dyadic chatbot does not know how to coordinate with many members in a chat group  It lacks the social ability of knowing when it is more suitable to answer or not  Also, note that we are not considering here the ones that would use this social ability as an advantage in the conversation, because if the chatbot is doing with this intention, it means that the chatbot was designed to be aware of the social issues regarding a chat with multiple members, which is not the case of a dyadic chatbot  Most existing chatbots, from the first system, ELIZA BIBREF11 , until modern state-of-the-art ones fall into this category 
Multiparty Conversations
In multiparty conversations between people and computer systems, natural language becomes the communication protocol exchanged not only by the human users, but also among the bots themselves  When every actor, computer or user, understands human language and is able to engage effectively in a conversation, a new, universal computer protocol of communication is feasible, and one for which people are extremely good at 
There are many differences between dyadic and multiparty conversations, but chiefly among them is turn-taking, that is, how a participant determines when it is appropriate to make an utterance and how that is accomplished  There are many social settings, such as assemblies, debates, one-channel radio communications, and some formal meetings, where there are clear and explicit norms of who, when, and for long a participant can speak 
The state of the art for the creation of chatbots that can participate on multiparty conversations currently is a combination of the research on the creation of chatbots and research on the coordination or governance of multi-agents systems  A definition that mixes both concepts herein present is: A chatbot is an agent that interacts through natural language  Although these areas complement each other, there is a lack of solutions for creating multiparty-aware chatbots or governed chatbots, which can lead to higher degree of system trust 
Multi-Dyadic Chatbots
Turn-taking in generic, multiparty spoken conversation has been studied by, for example, Sacks et al  BIBREF18   In broad terms, it was found that participants in general do not overlap their utterances and that the structure of the language and the norms of conversation create specific moments, called transition-relevance places, where turns can occur  In many cases, the last utterances make clear to the participants who should be the next speaker (selected-next-speaker), and he or she can take that moment to start to talk  Otherwise, any other participant can start speaking, with preference for the first starter to get the turn; or the current speaker can continue BIBREF18  
A key part of the challenge is to determine whether the context of the conversation so far have or have not determined the next speaker  In its simplest form, a vocative such as the name of the next speaker is uttered  Also, there is a strong bias towards the speaker before the current being the most likely candidate to be the next speaker 
In general the detection of transition-relevance places and of the selected-next-speaker is still a challenge for speech-based machine conversational systems  However, in the case of text message chats, transition-relevance places are often determined by the acting of posting a message, so the main problem facing multiparty-enabled textual chatbots is in fact determining whether there is and who is the selected-next-speaker  In other words, chatbots have to know when to shut up  Bohus and Horowitz BIBREF19 have proposed a computational probabilistic model for speech-based systems, but we are not aware of any work dealing with modeling turn-taking in textual chats 
Coordination of Multi-Agent Systems
A multi-agent system (MAS) can be defined as a computational environment in which individual software agents interact with each other, in a cooperative manner, or in a competitive manner, and sometimes autonomously pursuing their individual goals  During this process, they access the environment's resources and services and occasionally produce results for the entities that initiated these software agents  As the agents interact in a concurrent, asynchronous and decentralized manner, this kind of system can be categorized as a complex system BIBREF20  
Research in the coordination of multi-agent systems area does not address coordination using natural dialogue, as usually all messages are structured and formalized so the agents can reason and coordinate themselves  On the other hand, chatbots coordination have some relations with general coordination mechanisms of multi-agent systems in that they specify and control interactions between agents  However, chatbots coordination mechanisms is meant to regulate interactions and actions from a social perspective, whereas general coordination languages and mechanisms focus on means for expressing synchronization and coordination of activities and exchange of information, at a lower computational level 
In open multi-agent systems the development takes place without a centralized control, thus it is necessary to ensure the reliability of these systems in a way that all the interactions between agents will occur according to the specification and that these agents will obey the specified scenario  For this, these applications must be built upon a law-governed architecture 
Minsky published the first ideas about laws in 1987 BIBREF21   Considering that a law is a set of norms that govern the interaction, afterwards, he published a seminal paper with the Law-Governed Interaction (LGI) conceptual model about the role of interaction laws on distributed systems BIBREF22   Since then, he conducted further work and experimentation based on those ideas BIBREF23   Although at the low level a multiparty conversation system is a distributed system and the LGI conceptual model can be used in a variety of application domains, it is composed of abstractions basically related to low level information about communication issues of distributed systems (like the primitives disconnected, reconnected, forward, and sending or receiving of messages), lacking the ability to express high level information of social systems 
Following the same approach, the Electronic Institution (EI) BIBREF24 solution also provides support for interaction norms  An EI has a set of high-level abstractions that allow for the specification of laws using concepts such as agent roles, norms and scenes 
Still at the agent level but more at the social level, the XMLaw description language and the M-Law framework BIBREF25 BIBREF26 were proposed and developed to support law-governed mechanism  They implement a law enforcement approach as an object-oriented framework and it allows normative behavior through the combination between norms and clocks  The M-Law framework BIBREF26 works by intercepting messages exchanged between agents, verifying the compliance of the messages with the laws and subsequently redirecting the message to the real addressee, if the laws allow it  If the message is not compliant, then the mediator blocks the message and applies the consequences specified in the law, if any  They are called laws in the sense that they enforce the norms, which represent what can be done (permissions), what cannot be done (prohibitions) and what must be done (obligations) 
Coordinated Aware Chatbots in a Multiparty Conversation
With regard to chatbot engines, there is a lack of research directed to building coordination laws integrated with natural language  To the best of our knowledge, the architecture proposed in this paper is the first one in the state of the art designed to support the design and development of coordinated aware chatbots in a multiparty conversation 
Types of Architectures
There are mainly three types of architectures when building conversational systems: totally rule-oriented, totally data-oriented, and a mix of rules and data-oriented 
Rule-oriented
A rule-oriented architecture provides a manually coded reply for each recognized utterance  Classical examples of rule-based chatbots include Eliza and Parry  Eliza could also extract some words from sentences and then create another sentence with these words based on their syntatic functions  It was a rule-based solution with no reasoning  Eliza could not "understand" what she was parsing  More sophisticated rule-oriented architectures contain grammars and mappings for converting sentences to appropriate sentences using some sort of knowledge  They can be implemented with propositional logic or first-order logic (FOL)  Propositional logic assumes the world contains facts (which refer to events, phenomena, symptoms or activities)  Usually, a set of facts (statements) is not sufficient to describe a domain in a complete manner  On the other hand, FOL assumes the world contains Objects (e g , people, houses, numbers, etc ), Relations (e g  red, prime, brother of, part of, comes between, etc ), and Functions (e g  father of, best friend, etc ), not only facts as in propositional logic  Moreover, FOL contains predicates, quantifiers and variables, which range over individuals (which are domain of discourse) 
Prolog (from French: Programmation en Logique) was one of the first logic programming languages (created in the 1970s), and it is one of the most important languages for expressing phrases, rules and facts  A Prolog program consists of logical formulas and running a program means proving a theorem  Knowledge bases, which include rules in addition to facts, are the basis for most rule-oriented chatbots created so far 
In general, a rule is presented as follows: DISPLAYFORM0
Prolog made it possible to perform the language of Horn clauses (implications with only one conclusion)  The concept of Prolog is based on predicate logic, and proving theorems involves a resolute system of denials  Prolog can be distinguished from classic programming languages due to its possibility of interpreting the code in both a procedural and declarative way  Although Prolog is a set of specifications in FOL, it adopts the closed-world assumption, i e  all knowledge of the world is present in the database  If a term is not in the database, Prolog assumes it is false 
In case of Prolog, the FOL-based set of specifications (formulas) together with the facts compose the knowledge base to be used by a rule-oriented chatbot  However an Ontology could be used  For instance, OntBot BIBREF27 uses mapping technique to transform ontologies and knowledge into relational database and then use that knowledge to drive its chats  One of the main issues currently facing such a huge amount of ontologies stored in a database is the lack of easy to use interfaces for data retrieval, due to the need to use special query languages or applications 
In rule-oriented chatbots, the degree of intelligent behavior depends on the knowledge base size and quality (which represents the information that the chatbot knows), poor ones lead to weak chatbot responses while good ones do the opposite  However, good knowledge bases may require years to be created, depending on the domain 
Data-oriented
As opposed to rule-oriented architectures, where rules have to be explicitly defined, data-oriented architectures are based on learning models from samples of dialogues, in order to reproduce the behavior of the interaction that are observed in the data  Such kind of learning can be done by means of machine learning approach, or by simply extracting rules from data instead of manually coding them 
Among the different technologies on which these system can be based, we can highlight classical information retrieval algorithms, neural networks BIBREF28 , Hidden Markov Models (HMM) BIBREF29 , and Partially Observable Markov Decision Process (POMDP) BIBREF30   Examples include Cleverbot and Tay BIBREF31   Tay was a chatbot developed by Microsoft that after one day live learning from interaction with teenagers on Twitter, started replying impolite utterances  Microsoft has developed others similar chatbots in China (Xiaoice) and in Japan (Rinna)  Microsoft has not associated its publications with these chatbots, but they have published a data-oriented approach BIBREF32 that proposes a unified multi-turn multi-task spoken language understanding (SLU) solution capable of handling multiple context sensitive classification (intent determination) and sequence labeling (slot filling) tasks simultaneously  The proposed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature layers and globally normalized sequence modeling components 
A survey of public available corpora for can be found in BIBREF33   A corpus can be classified into different categories, according to: the type of data, whether it is spoken dialogues, transcripts of spoken dialogues, or directly written; the type of interaction, if it is human-human or human-machine; and the domain, whether it is restricted or unconstrained  Two well-known corpora are the Switchboard dataset, which consists of transcripts of spoken, unconstrained, dialogues, and the set of tasks for the Dialog State Tracking Challenge (DSTC), which contain more constrained tasks, for instance the restaurant and travel information sets 
Rule and Data-oriented
The model of learning in current A L I C E  BIBREF13 is incremental or/and interactive learning because a person monitors the robot's conversations and creates new AIML content to make the responses more appropriate, accurate, believable, "human", or whatever he/she intends  There are algorithms for automatic detection of patterns in the dialogue data and this process provides the person with new input patterns that do not have specific replies yet, permitting a process of almost continuous supervised refinement of the bot 
As already mentioned, A L I C E  consists of roughly 41,000 elements called categories which is the basic unit of knowledge in AIML  Each category consists of an input question, an output answer, and an optional context  The question, or stimulus, is called the pattern  The answer, or response, is called the template  The two types of optional context are called that and topic  The keyword that refers to the robot's previous utterance  The AIML pattern language consists only of words, spaces, and the wildcard symbols "_" and "*"  The words may consist only of letters and numerals  The pattern language is case invariant  Words are separated by a single space, and the wildcard characters function like words, similar to the initial pattern matching strategy of the Eliza system  More generally, AIML tags transform the reply into a mini computer program which can save data, activate other programs, give conditional responses, and recursively call the pattern matcher to insert the responses from other categories  Most AIML tags in fact belong to this template side sublanguage BIBREF13  
AIML language allows:
Symbolic reduction: Reduce complex grammatical forms to simpler ones 
Divide and conquer: Split an input into two or more subparts, and combine the responses to each 
Synonyms: Map different ways of saying the same thing to the same reply 
Spelling or grammar corrections: the bot both corrects the client input and acts as a language tutor 
Detecting keywords anywhere in the input that act like triggers for a reply 
Conditionals: Certain forms of branching to produce a reply 
Any combination of (1)-(6) 
When the bot chats with multiple clients, the predicates are stored relative to each client ID  For example, the markup INLINEFORM0 set name INLINEFORM1 "name" INLINEFORM2 Matthew INLINEFORM3 /set INLINEFORM4 stores the string Matthew under the predicate named "name"  Subsequent activations of INLINEFORM5 get name="name" INLINEFORM6 return "Matthew"  In addition, one of the simple tricks that makes ELIZA and A L I C E  so believable is a pronoun swapping substitution  For instance:
U: My husband would like to invest with me 
S: Who else in your family would like to invest with you?
Types of Intentions
According to the types of intentions, conversational systems can be classified into two categories: a) goal-driven or task oriented, and b) non-goal-driven or end-to-end systems 
In a goal-driven system, the main objective is to interact with the user so that back-end tasks, which are application specific, are executed by a supporting system  As an example of application we can cite technical support systems, for instance air ticket booking systems, where the conversation system must interact with the user until all the required information is known, such as origin, destination, departure date and return date, and the supporting system must book the ticket  The most widely used approaches for developing these systems are Partially-observed Decision Processes (POMDP) BIBREF30 , Hidden Markov Models (HMM) BIBREF29 , and more recently, Memory Networks BIBREF28   Given that these approaches are data-oriented, a major issue is to collect a large corpora of annotated task-specific dialogs  For this reason, it is not trivial to transfer the knowledge from one to domain to another  In addition, it might be difficult to scale up to larger sets of tasks 
Non-goal-driven systems (also sometimes called reactive systems), on the other hand, generate utterances in accordance to user input, e g  language learning tools or computer games characters  These systems have become more popular in recent years, mainly owning to the increase of popularity of Neural Networks, which is also a data-oriented approach  The most recent state of the art to develop such systems have employed Recurrent Neural Networs (RNN) BIBREF34 , Dynamic Context-Sensitive Generation BIBREF35 , and Memory Networks BIBREF36 , just to name a few  Nevertheless, probabilistic methods such as Hidden Topic Markov Models (HTMM) BIBREF37 have also been evaluated  Goal-driven approach can create both pro-active and reactive chatbots, while non-goal-driven approach creates reactive chatbots  In addition, they can serve as a tool to goal-driven systems as in BIBREF28   That is, when trained on corpora of a goal-driven system, non-goal-driven systems can be used to simulate user interaction to then train goal-driven models 
Types of Context Reasoning
A dialogue system may support the context reasoning or not  Context reasoning is necessary in many occasions  For instance, when partial information is provided the chatbot needs to be able to interact one or more turns in order to get the complete information in order to be able to properly answer  In BIBREF38 , the authors present a taxonomy of errors in conversational systems  The ones regarding context-level errors are the ones that are perceived as the top-10 confusing and they are mainly divided into the following:
Excess/lack of proposition: the utterance does not provide any new proposition to the discourse context or provides excessive information than required 
Contradiction: the utterance contains propositions that contradict what has been said by the system or by the user 
Non-relevant topic: the topic of the utterance is irrelevant to the current context such as when the system suddenly jumps to some other topic triggered by some particular word in the previous user utterance 
Unclear relation: although the utterance might relate to the previous user utterance, its relation to the current topic is unclear 
Topic switch error: the utterance displays the fact that the system missed the switch in topic by the user, continuing with the previous topic 
Rule-oriented
In the state of the art most of the proposed approaches for context reasoning lies on rules using logics and knowledge bases as described in the Rule-oriented architecture sub-section  Given a set of facts extracted from the dialogue history and encoded in, for instance, FOL statements, a queries can be posed to the inference engine and produce answers  For instance, see the example in Table TABREF37   The sentences were extracted from BIBREF36 (which does not use a rule-oriented approach), and the first five statements are their respective facts  The system then apply context reasoning for the query Q: Where is the apple 
If statements above are received on the order present in Table TABREF37 , if the query Q: Where is the apple is sent, the inference engine will produce the answer A: Bedroom (i e , the statement INLINEFORM0 is found by the model and returned as True) 
Nowadays, the most common way to store knowledge bases is on triple stores, or RDF (Resource Description Framework) stores  A triple store is a knowledge base for the storage and retrieval of triples through semantic queries  A triple is a data entity composed of subject-predicate-object, like "Sam is at the kitchen" or "The apple is with Sam", for instance  A query language is needed for storing and retrieving data from a triple store  While SPARQL is a RDF query language, Rya is an open source scalable RDF triple store built on top of Apache Accumulo  Originally developed by the Laboratory for Telecommunication Sciences and US Naval Academy, Rya is currently being used by a number of american government agencies for storing, inferencing, and querying large amounts of RDF data 
A SPARQL query has a SQL-like syntax for finding triples matching specific patterns  For instance, see the query below  It retrieves all the people that works at IBM and lives in New York:
SELECT ?people
WHERE {
?people <worksAt> <IBM>  
?people <livesIn> <New York> 
}
Since triple stores can become huge, Rya provides three triple table index BIBREF39 to help speeding up queries:
SPO: subject, predicate, object
POS: predicate, object, subject
OSP: object, subject, predicate
While Rya is an example of an optimized triple store, a rule-oriented chatbot can make use of Rya or any triple store and can call the semantic search engine in order to inference and generate proper answers 
Data-oriented
Recent papers have used neural networks to predict the next utterance on non-goal-driven systems considering the context, for instance with Memory Networks BIBREF40   In this work BIBREF36 , for example the authors were able to generate answers for dialogue like below:
Sam walks into the kitchen 
Sam picks up an apple 
Sam walks into the bedroom 
Sam drops the apple 
Q: Where is the apple?
A: Bedroom
Sukhbaatar's model represents the sentence as a vector in a way that the order of the words matter, and the model encodes the temporal context enhancing the memory vector with a matrix that contains the temporal information  During the execution phase, Sukhbaatar's model takes a discrete set of inputs INLINEFORM0 that are to be stored in the memory, a query INLINEFORM1 , and outputs an answer INLINEFORM2   Each of the INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 contains symbols coming from a dictionary with INLINEFORM6 words  The model writes all INLINEFORM7 to the memory up to a fixed buffer size, and then finds a continuous representation for the INLINEFORM8 and INLINEFORM9   The continuous representation is then processed via multiple computational steps to output INLINEFORM10   This allows back propagation of the error signal through multiple memory accesses back to the input during training  Sukhbaatar's also presents the state of the art of recent efforts that have explored ways to capture dialogue context, treated as long-term structure within sequences, using RNNs or LSTM-based models  The problem of this approach is that it is has not been tested for goal-oriented systems  In addition, it works with a set of sentences but not necessary from multi-party bots 
Platforms
Regarding current platforms to support the development of conversational systems, we can categorize them into three types: platforms for plugging chatbots, for creating chatbots and for creating service chatbots  The platforms for plugging chatbots provide tools for integrating them another system, like Slack  The chatbots need to receive and send messages in a specific way, which depends on the API and there is no support for actually helping on building chatbots behavior with natural language understanding  The platforms for creating chatbots mainly provide tools for adding and training intentions together with dialogue flow specification and some entities extraction, with no reasoning support  Once the models are trained and the dialogue flow specified, the chatbots are able to reply to the received intention  The platforms for creating service chatbots provide the same functionalities as the last one and also provide support for defining actions to be executed by the chatbots when they are answering to an utterance  Table TABREF43 summarizes current platforms on the market accordingly to these categories  There is a lack on platforms that allow to create chatbots that can be coordinated in a multiparty chat with governance or mediation 
A Conceptual Architecture for Multiparty-Aware Chatbots
In this section the conceptual architecture for creating a hybrid rule and machine learning-based MPCS is presented  The MPCS is defined by the the entities and relationships illustrated in Fig  FIGREF44 which represents the chatbot's knowledge  A Chat Group contains several Members that join the group with a Role  The role may constrain the behavior of the member in the group  Chatbot is a type of Role, to differentiate from persons that may also join with different roles  For instance, a person may assume the role of the owner of the group, or someone that was invited by the owner, or a domain role like an expert, teacher or other 
When a Member joins the Chat Group, it/he/she can send Utterances  The Member then classifies each Utterance with an Intent which has a Speech Act  The Intent class, Speech Act class and the Intent Flow trigger the Action class to be executed by the Member that is a Chatbot  The Chatbots associated to the Intention are the only ones that know how to answer to it by executing Actions  The Action, which implements one Speech Act, produces answers which are Utterances, so, for instance, the Get_News action produces an Utterance for which Intention's speech act is Inform_News  The Intent Flow holds the intent's class conversation graph which maps the dialog state as a decision tree  The answer's intention class is mapped in the Intent Flow as a directed graph G defined as following: DISPLAYFORM0
From the graph definitions, INLINEFORM0 is for vertices and INLINEFORM1 is for relations, which are the arrows in the graph  And in Equation EQREF46 :
INLINEFORM0 is the set of intentions pairs,
INLINEFORM0 is the set of paths to navigate through the intentions,
INLINEFORM0 is the arrow's head, and
INLINEFORM0 is the arrow's tail 
This arrow represents a turn from an utterance with INLINEFORM0 intention class which is replying to an utterance with INLINEFORM1 intention class to the state which an utterance with INLINEFORM2 intention's class is sent 
INLINEFORM0 is the intention class of the answer to be provided to the received INLINEFORM1 intention class 
In addition, each intent's class may refer to many Entities which, in turn, may be associated to several Features  For instance, the utterance
"I would like to invest USD10,000 in Savings Account for 2 years"
contains one entity – the Savings Account's investment option – and two features – money (USD10,000) and period of time (2 years)  The Intent Flow may need this information to choose the next node which will give the next answer  Therefore, if the example is changed a little, like
"I would like to invest in Savings Account",
INLINEFORM0 is constrained by the "Savings Account" entity which requires the two aforementioned features  Hence, a possible answer by one Member of the group would be
"Sure, I can simulate for you, what would be the initial amount and the period of time of the investment?"
With these conceptual model's elements, a MPCS system can be built with multiple chatbots  Next subsection further describes the components workflow 
Workflow
Figure FIGREF48 illustrates from the moment that an utterance is sent in a chat group to the moment a reply is generated in the same chat group, if the case  One or more person may be in the chat, while one or more chatbots too  There is a Hub that is responsible for broadcasting the messages to every Member in the group, if the case  The flow starts when a Member sends the utterance which goes to the Hub and, if allowed, is broadcasted  Many or none interactions norms can be enforced at this level depending on the application  Herein, a norm can be a prohibition, obligation or permission to send an utterance in the chat group 
Once the utterance is broadcasted, a chatbot needs to handle the utterance  In order to properly handle it, the chatbot parses the utterance with several parsers in the Parsing phase: a Topic Classifier, the Dependency Parsing, which includes Part-of-Speech tags and semantics tags, and any other that can extract metadata from the utterance useful for the reasoning  All these metadata, together with more criteria, may be used in the Frame parsing which is useful for context reasoning  All knowledge generated in this phase can be stored in the Context  Then, the Intent Classifier tries to detect the intent class of the utterance  If detected, the Speech Act is also retrieved  And an Event Detector can also check if there is any dialog inconsistency during this phase 
After that, the Filtering phase receives the object containing the utterance, the detected intent, and all metadata extracted so far and decides if an action should be performed to reply to the utterance  If yes, it is sent to the Acting phase which performs several steps  First the Action Classifier tries to detect the action to be performed  If detected, the action is executed  At this step, many substeps may be performed, like searching for an information, computing maths, or generating information to create the answer  All of this may require a search in the Context and also may activate the Error Detector component to check if the dialog did not run into a wrong state  After the answer is generated, the Filtering phase is activated again to check if the reply should be really sent  If so, it is sent to the Hub which, again may check if it can be broadcasted before actually doing it 
The topic classifier is domain-dependent and is not mandatory  However, the chatbot can better react when the intent or action is not detected, which means that it does not know how to answer  Many reasons might explain this situation: the set of intents might be incomplete, the action might not have produced the proper behavior, misunderstanding might happen, or the chatbot was not designed to reply to a particular topic  In all cases, it must be able to produce a proper reply, if needed  Because this might happen throughout the workflow, the sooner that information is available, the better the chatbot reacts  Therefore it is one of the first executions of the flow 
Dependency is the notion that linguistic units, e g  words, are connected to each other by directed links  The (finite) verb is taken to be the structural center of clause structure  All other syntactic units (words) are either directly or indirectly connected to the verb in terms of the directed links, which are called dependencies  It is a one-to-one correspondence: for every element (e g  word or morph) in the sentence, there is exactly one node in the structure of that sentence that corresponds to that element  The result of this one-to-one correspondence is that dependency grammars are word (or morph) grammars  All that exist are the elements and the dependencies that connect the elements into a structure  Dependency grammar (DG) is a class of modern syntactic theories that are all based on the dependency relation 
Semantic dependencies are understood in terms of predicates and their arguments  Morphological dependencies obtain between words or parts of words  To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, a tagset that consists of twelve universal part-of-speech categories was proposed BIBREF41  
Dependency parsers have to cope with a high degree of ambiguity and nondeterminism which let to different techniques than the ones used for parsing well-defined formal languages  Currently the mainstream approach uses algorithms that derive a potentially very large set of analyses in parallel and when disambiguation is required, this approach can be coupled with a statistical model for parse selection that ranks competing analyses with respect to plausibility BIBREF42  
Below we present an example of a dependency tree for the utterance:
"I want to invest 10 thousands":
[s]""blue[l]:black
"tree": {
"want VERB ROOT": {
"I PRON nsubj": {},
"to ADP mark": {},
"invest VERB nmod": {
"thousands NOUN nmod": {
"10 NUM nummod": {}
}
}
}
The coarse-grained part-of-speech tags, or morphological dependencies (VERB, PRON, ADP, NOUN and NUM) encode basic grammatical categories and the grammatical relationships (nsubjs, nmod, nummod) are defined in the Universal Dependencies project BIBREF41  
In this module, the dependency tree generated is used together with a set of rules to extract information that is saved in the context using the Frame-based approach  This approach fills the slots of the frame with the extracted values from the dialogue  Frames are like forms and slots are like fields  Using the knowledge's conceptual model, the fields are represented by the elements Entities and Features  In the dependency tree example, the entity would be the implicit concept: the investment option, and the feature is the implicit concept: initial amount – 10 thousands  Since the goal is to invest, and there are more entities needed for that (i e , fields to be filled), the next node in the Intention Flow tree would return an utterance which asks the user the time of investment, if he/she has not provided yet 
This module could be implemented using different approaches according to the domain, but tree search algorithms will be necessary for doing the tree parsing 
The Intent Classifier component aims at recognizing not only the Intent but the goal of the utterance sent by a Member, so it can properly react  The development of an intent classifier needs to deal with the following steps:
i) the creation of dataset of intents, to train the classification algorithm;
ii) the design of a classification algorithm that provides a reasonable level of accuracy;
iii) the creation of dataset of trees of intents, the same as defined in i) and which maps the goals;
iv) the design of a plan-graph search algorithm that maps the goal's state to a node in the graph;
There are several approaches to create training sets for dialogues: from an incremental approach to crowdsourcing  In the incremental approach, the Wizard of Oz method can be applied to a set of potential users of the system, and from this study, a set of questions that the users asked posted to the `fake' system can be collected  These questions have to be manually classified into a set of intent classes, and used to train the first version of the system  Next, this set has to be increased both in terms of number of classes and samples per class 
The Speech Act Classifier can be implemented with many speech act classes as needed by the application  The more classes, the more flexible the chatbot is  It can be built based on dictionaries, or a machine learning-based classifier can be trained  In the table below we present the main and more general speech act classes BIBREF43 used in the Chatbots with examples to differentiate one from another:
There are at least as many Action classes as Speech Act classes, since the action is the realization of a speech act  The domain specific classes, like "Inform_News" or "Inform_Factoids", enhance the capabilities of answering of a chatbot 
The Action Classifier can be defined as a multi-class classifier with the tuple DISPLAYFORM0
where INLINEFORM0 is the intent of the answer defined in ( EQREF46 ), INLINEFORM1 is the speech act of the answer, INLINEFORM2 and INLINEFORM3 are the sets of entities and features needed to produce the answer, if needed, respectively 
This component is responsible for implementing the behavior of the Action class  Basic behaviors may exist and be shared among different chatbots, like the ones that implement the greetings, thanks or not understood  Although they can be generic, they can also be personalized to differentiate the bot from one another and also to make it more "real"  Other cases like to inform, to send a query, to send a proposal, they are all domain-dependent and may require specific implementations 
Anyway, figure FIGREF59 shows at the high level the generic workflow  If action class detected is task-oriented, the system will implement the execution of the task, say to guide a car, to move a robot's arm, or to compute the return of investments  The execution might need to access an external service in the Internet in order to complete the task, like getting the inflation rate, or the interest rate, or to get information about the environment, or any external factor  During the execution or after it is finished, the utterance is generated as a reply and, if no more tasks are needed, the action execution is finished 
In the case of coordination of chatbots, one or more chatbots with the role of mediator may exist in the chat group and, at this step, it is able to invite one or more chatbots to the chat group and it is also able to redirect the utterances, if the case 
The proposed architecture addresses the challenges as the following:
What is the message/utterance about? solved by the Parsing phase;
Who should reply to the utterance? solved by the Filtering phase and may be enforced by the Hub;
How the reply should be built/generated? solved by the Acting phase;
When should the reply be sent? may be solved by the Acting phase or the Filtering phase, and may be enforced by the Hub;
And Context and Logging module is used throughout all phases 
Architecture Implementation and Evaluation
This section presents one implementation of the conceptual architecture presented in last section  After many refactorings, a framework called SABIA (Speech-Act-Based Intelligent Agents Framework) has been developed and CognIA (Cognitive Investment Advisor) application has been developed as an instantiation of SABIA framework  We present then the accuracy and some automated tests of this implementation 
Speech-Act-based Intelligent Agents Framework
SABIA was developed on top of Akka middleware  Akka is a toolkit and runtime that implements the Actor Model on the JVM  Akka's features, like concurrency, distributed computing, resilience, and message-passing were inspired by Erlang's actor model BIBREF44 BIBREF45   The actor model is a mathematical model of concurrent computation that treats "actors" as the universal primitives of concurrent computation  In response to a message that it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next received message  Actors may modify private state, but can only affect each other through messages (avoiding the need for any locks)  Akka middleware manages the actors life cycle and actors look up by theirs name, locally or remotely 
We implemented each Member of the Chat Group as an Actor by extending the UntypedActor class of Akka middleware  Yet, we created and implemented the SabiaActorSystem as a singleton (i e , a single instance of it exists in the system) BIBREF46 that has a reference to Akka's ActorSystem  During SabiaActorSystem's initialization, all parsers that consume too much memory during their initialization to load models are instantiated as singletons  In this way, we save time on their calls during the runtime  Moreover, all chat group management, like to join or leave the group, or to broadcast or filter a message at the Hub level is implemented in SABIA through the Chat Group behavior 
This is implemented in SABIA as a singleton that is initialized during the SabiaActorSystem initialization with the URL of the service that implements the dependency parsing and is used on each utterance's arrival through the execution of the tagUtterance method  The service must retrieve a JSON Object with the dependency tree which is then parsed using depth-first search 
SABIA does not support invariants for frame parsing  We are leaving this task to the instantiated application 
There are two intent classifiers that can be loaded with trained models in order to be ready to be used at runtime: the 1-nearest-neighbor (1NN) and the SVM-based classifier 
SABIA implements the Action Classifier assuming that the application uses a relational database with a data schema that implements the conceptual model presented in Figure FIGREF44   Then the invariants parts that use SQL are already present and the application only needs to implement the database connection and follow the required data schema 
SABIA provides partial implemented behavior for the Action through the Template method design pattern BIBREF46 , which implements the invariants parts of the action execution and leaves placeholders for customization 
CognIA: A Cognitive Investment Advisor
We developed CognIA, which is an instantiation of Sabia framework  A conversation is composed of a group chat that can contain multiple users and multiple chatbots  This example, in particular, has a mediator that can help users on financial matters, more specifically on investment options  For example, consider the following dialogue in the table below:
The Table TABREF71 shows an example that uses the mixed-initiative dialogue strategy, and a dialogue mediator to provide coordination control  In this example of an application, there are many types of intentions that should be answered: Q&A (question and answer) about definitions, investment options, and about the current finance indexes, simulation of investments, which is task-oriented and requires computation, and opinions, which can be highly subjective 
In Table SECREF72 , we present the interaction norms that were needed in Cognia  The Trigger column describes the event that triggers the Behavior specified in the third column  The Pre-Conditions column specifies what must happen in order to start the behavior execution  So, for instance, line 2, when the user sends an utterance in the chat group, an event is triggered and, if the utterance's topic is CDB (Certificate of Deposit which is a fixed rate investment) or if it is about the Savings Account investment option and the speech act is not Query_Calculation and the CDB and Savings Account members are not in the chat, then the behavior is activated  The bot members that implement these behaviors are called cdbguru and poupancaguru  Therefore these names are used when there is a mention 
Note that these interactions norms are not explicitly defined as obligations, permissions, and prohibitions  They are implict from the behavior described  During this implementation, we did not worry about explicitly defining the norms, because the goal was to evaluate the overall architecture, not to enhance the state of the art on norms specification for conversational systems  In addition, CognIA has only the presented interaction norms defined in Table SECREF72 , which is a very small set that that does not required model checking or verification of conflicts 
|p2cm|p5 0cm|p5 0cm|Cognia Interaction NormsCognia Interaction Norms
Trigger Pre-Conditions Behavior
On group chat creation Cognia chatbot is available Cognia chatbot joins the chat with the mediator role and user joins the chat with the owner_user role
On utterance sent by user Utterance's topic is CDB (cdbguru) or Savings Account (poupancaguru) and speech act is not Query_Calculation and they are not in the chat Cognia invites experts to the chat and repeats the utterance to them
On utterance sent by user Utterance's topic is CDB (cdbguru) or Savings Account (poupancaguru) and speech act is not Query_Calculation and they are in the chat Cognia waits for while and cdbguru or poupancaguru respectively handles the utterance  If they don't understand, they don't reply
On utterance sent by the experts If Cognia is waiting for them and has received both replies Cognia does not wait anymore
On utterance sent Utterance mentions cdbguru or poupancaguru cdbguru or poupancaguru respectively handles the utterance
On utterance sent Utterance mentions cdbguru or poupancaguru and they don't reply after a while and speech act is Query_Calculation Cognia sends I can only chat about investments   
On utterance sent Utterance mentions cdbguru or poupancaguru and they don't reply after while and speech act is not Query_Calculation Cognia sends I didn't understand
On utterance sent Utterance's speech act is Query_Calculation and period or initial amount of investment were not specified Cognia asks the user the missing information
On utterance sent Utterance's speech act is Query_Calculation and period and initial amount of investment were specified and the experts are not in the chat Cognia invites experts to the chat and repeats the utterance to them
On utterance sent Utterance's speech act is Query_Calculation and period and initial amount of investment were specified and the experts are in the chat Cognia repeats the utterance to experts
On utterance sent Utterance's speech act is Query_Calculation Cognia extracts variables and saves the context
On utterance sent Utterance's speech act is Query_Calculation and the experts are in the chat and the experts are mentioned Experts extract information, save in the context, compute calculation and send information
On utterance sent Utterance's speech act is Inform_Calculation and Cognia received all replies Cognia compares the results and inform comparison
On utterance sent Utterance mentions a chatbot but has no other text The chatbot replies How can I help you?
On utterance sent Utterance is not understood and speech act is Question The chatbot replies I don't know    I can only talk about topic X
On utterance sent Utterance is not understood and speech act is not Question The chatbot replies I didn't understand
On utterance sent Utterance's speech act is one of { Greetings, Thank, Bye } All chatbots reply to utterance
On group chat end All chatbots leave the chat, and the date and time of the end of chat is registered
We instantiated SABIA to develop CognIA as follows: the Mediator, Savings Account, CDB and User Actors are the Members of the Chat Group  The Hub was implemented using two servers: Socket io and Node JS which is a socket client of the Socket io server  The CognIA system has also one Socket Client for receiving the broadcast and forwarding to the Group Chat Manager  The former will actually do the broadcast to every member after enforcing the norms that applies specified in Table SECREF72   Each Member will behave according to this table too  For each user of the chat group, on a mobile or a desktop, there is its corresponding actor represented by the User Actor in the figure  Its main job is to receive Akka's broadcast and forward to the Socket io server, so it can be finally propagated to the users 
All the intents, actions, factual answers, context and logging data are saved in DashDB (a relational Database-as-a-Service system)  When an answer is not retrieved, a service which executes the module Search Finance on Social Media on a separate server is called  This service was implemented with the assumption that finance experts post relevant questions and answers on social media  Further details are explained in the Action execution sub-section 
We built a small dictionary-based topic classifier to identify if an utterance refers to finance or not, and if it refers to the two investment options (CDB or Savings Account) or not 
The dependency parsing is extremely important for computing the return of investment when the user sends an utterance with this intention  Our first implementation used regular expressions which led to a very fragile approach  Then we used a TensorFlow implementation BIBREF47 of a SyntaxNet model for Portuguese and used it to generate the dependency parse trees of the utterances  The SyntaxNet model is a feed-forward neural network that operates on a task-specific transition system and achieves the state-of-the-art on part-of-speech tagging, dependency parsing and sentence compression results BIBREF48   Below we present output of the service for the utterance:
"I want to invest 10 thousands in 40 months":
[s]""blue[l]:black
{ "original": "I would like to invest 10 thousands in 40 months",
"start_pos": [
23,
32],
"end_pos": [
27,
33],
"digits": [
10000,
40],
"converted": "I would like to invest 10000 in 40 months",
"tree": {
"like VERB ROOT": {
"I PRON nsubj": {},
"would MD aux":{
"invest VERB xcomp":{
"to TO aux": {},
"10000 NUM dobj": {},
"in IN prep": {
"months NOUN pobj":{
"40 NUM num": {}}}}}}}
The service returns a JSON Object containing six fields: original, start_pos, end_pos, digits, converted and tree  The original field contains the original utterance sent to the service  The converted field contains the utterance replaced with decimal numbers, if the case (for instance, "10 thousands" was converted to "10000" and replaced in the utterance)  The start_pos and end_pos are arrays that contain the start and end char positions of the numbers in the converted utterance  While the tree contains the dependency parse tree for the converted utterance 
Given the dependency tree, we implemented the frame parsing which first extracts the entities and features from the utterance and saves them in the context  Then, it replaces the extracted entities and features for reserved characters 
extract_period_of_investment (utteranceTree) [1] [t] numbersNodes INLINEFORM0 utteranceTree getNumbersNodes(); [t] foreach(numberNode in numbersNodes) do [t] parentsOfNumbersNode INLINEFORM1 numbersNode getParents() [t] foreach(parent in parentsOfNumbersNodes) do [t] if ( parent name contains { "day", "month", "year"} ) then [t] parentOfParent INLINEFORM2 parent getParent() [t] if ( parentOfParent is not null and
parentOfParent getPosTag==Verb and
parentOfParent name in investmentVerbsSet ) then [t] return numberNode
Therefore an utterance like "I would like to invest 10 thousands in 3 years" becomes "I would like to invest #v in #dt years"  Or "10 in 3 years" becomes "#v in #dt years", and both intents have the same intent class 
For doing that we implemented a few rules using a depth-first search algorithm combined with the rules as described in Algorithm UID79 , Algorithm UID79 and Algorithm UID79   Note that our parser works only for short texts on which the user's utterance mentions only one period of time and/ or initial amount of investment in the same utterance 
extract_initial_amount_of_investment (utteranceTree) [1] [t] numbersNodes INLINEFORM0 utteranceTree getNumbersNodes(); [t] foreach(numberNode in numbersNodes) do [t] parentsOfNumbersNode INLINEFORM1 numbersNode getParents() [t] foreach(parent in parentsOfNumbersNodes) do [t] if ( parent name does not contain { "day", "month", "year"} ) then [t] return numberNode
frame_parsing(utterance, utteranceTree) [1] [t] period INLINEFORM0 extract_period_of_investment (utteranceTree) [t] save_period_of_investment(period) [t] value INLINEFORM1 extract_initial_amount_of_investment (utteranceTree) [t] save_initial_amount_of_investment(value) [t] new_intent INLINEFORM2 replace(new_intent, period, "#dt") [t] new_intent INLINEFORM3 replace(new_intent, value, "#v")
In CognIA we have complemented the speech act classes with the ones related to the execution of specific actions  Therefore, if the chatbot needed to compute the return of investment, then, once it is computed, the speech act of the reply will be Inform_Calculation and the one that represents the query for that is Query_Calculation  In table TABREF81 we list the specific ones 
Given that there is no public dataset available with financial intents in Portuguese, we have employed the incremental approach to create our own training set for the Intent Classifier  First, we applied the Wizard of Oz method and from this study, we have collected a set of 124 questions that the users asked  Next, after these questions have been manually classified into a set of intent classes, and used to train the first version of the system, this set has been increased both in terms of number of classes and samples per class, resulting in a training set with 37 classes of intents, and a total 415 samples, with samples per class ranging from 3 to 37 
We have defined our classification method based on features extracted from word vectors  Word vectors consist of a way to encode the semantic meaning of the words, based on their frequency of co-occurrence  To create domain-specific word vectors, a set of thousand documents are needed related to desired domain  Then each intent from the training set needs to be encoded with its corresponding mean word vector  The mean word vector is then used as feature vector for standard classifiers 
We have created domain-specific word vectors by considering a set 246,945 documents, corresponding to of 184,001 Twitter posts and and 62,949 news articles, all related to finance  
The set of tweets has been crawled from the feeds of blog users who are considered experts in the finance domain  The news article have been extracted from links included in these tweets  This set contained a total of 63,270,124 word occurrences, with a vocabulary of 97,616 distinct words  With the aforementioned word vectors, each intent from the training set has been encoded with its corresponding mean word vector  The mean word vector has been then used as feature vector for standard classifiers 
As the base classifier, we have pursued with a two-step approach  In the first step, the main goal was to make use of a classifier that could be easily retrained to include new classes and intents  For this reason, the first implementation of the system considered an 1-nearest-neighbor (1NN) classifier, which is simply a K-nearest-neighbor classifier with K set to 1  With 1NN, the developer of the system could simply add new intents and classes to the classifier, by means of inserting new lines into the database storing the training set  Once we have considered that the training set was stable enough for the system, we moved the focus to an approach that would be able to provide higher accuracy rates than 1NN  For this, we have employed Support Vector Machines (SVM) with a Gaussian kernel, the parameters of which are optimized by means of a grid search 
We manually mapped the intent classes used to train the intent classifier to action classes and the dependent entities and features, when the case  Table TABREF85 summarizes the number of intent classes per action class that we used in CognIA 
For the majority of action classes we used SABIA's default behavior  For instance, Greet and Bye actions classes are implemented using rapport, which means that if the user says "Hi" the chatbot will reply "Hi" 
The Search News, Compute and Ask More classes are the ones that require specific implemention for CognIA as following:
Search News: search finance on social media service BIBREF49 , BIBREF50 receives the utterance as input, searches on previously indexed Twitter data for finance for Portuguese and return to the one which has the highest score, if found 
Ask More: If the user sends an utterance that has the intention class of simulating the return of investment, while not all variables to compute the return of investment are extracted from the dialogue, the mediator keeps asking the user these information before it actually redirects the query to the experts  This action then checks the state of the context given the specified intent flow as described in ( EQREF46 ) and ( EQREF57 ) in section SECREF4 to decide which variables are missing  For CognIA we manually added these dependencies on the database 
Compute: Each expert Chatbot implements this action according to its expertise  The savings account chatbot computes the formula ( EQREF90 ) and the certificate of deposit computes the formula ( EQREF92 )  Both are currently formulas for estimating in Brazil  DISPLAYFORM0
where INLINEFORM0 is the return of investment for the savings account, INLINEFORM1 is the initial value of investment, INLINEFORM2 is the savings account interest rate and INLINEFORM3 is the savings account rate base  DISPLAYFORM0
where INLINEFORM0 is the return of investment for certificate of deposit, INLINEFORM1 is the initial value of investment, INLINEFORM2 is the Interbank Deposit rate (DI in Portuguese), INLINEFORM3 is the ID's percentual payed by the bank (varies from 90% to 120%), INLINEFORM4 is the number of days the money is invested, and finally INLINEFORM5 is the income tax on the earnings 
Intention Classifier Accuracy
In Table TABREF95 we present the comparison of some distinct classification on the first version of the training set, i e  the set used to deploy the first classifier into the system  Roughly speaking, the 1NN classifier has been able to achieve a level of accuracy that is higher than other well-known classifiers, such as Logistic Regression and Naïve Bayes, showing that 1NN is suitable as a development classifier  Nevertheless, a SVM can perform considerable better than 1NN, reaching accuracies of about 12 percentage points higher, which demonstrates that this type of base classifier is a better choice to be deployed once the system is stable enough  It is worth mentioning that these results consider the leave-one-out validation procedure, given the very low number of samples in some classes 
As we mentioned, the use of an 1NN classifier has allowed the developer of the system to easily add new intent classes and samples whenever they judged it necessary, so that the system could present new actions, or the understanding of the intents could be improved  As a consequence, the initial training set grew from 37 to 63 classes, and from 415 to 659 samples, with the number of samples per class varying from 2 to 63  For visualizing the impact on the accuracy of the system, in Table TABREF96 we present the accuracy of the same classifiers used in the previous evaluation, in the new set  In this case, we observe some drop in accuracy for 1NN, showing that this classifier suffers in dealing with scalability  On the other hand, SVM has shown to scale very well to more classes and samples, since its accuracy kept at a very similar level than that with the other set, with a difference of about only 1 percentage point 
Testing SABIA
In this section, we describe the validation framework that we created for integration tests  For this, we developed it as a new component of SABIA's system architecture and it provides a high level language which is able to specify interaction scenarios that simulate users interacting with the deployed chatbots  The system testers provide a set of utterances and their corresponding expected responses, and the framework automatically simulates users interacting with the bots and collect metrics, such as time taken to answer an utterance and other resource consumption metrics (e g , memory, CPU, network bandwidth)  Our goal was to: (i) provide a tool for integration tests, (ii) to validate CognIA's implementation, and (iii) to support the system developers in understanding the behavior of the system and which aspects can be improved  Thus, whenever developers modify the system's source code, the modifications must first pass the automatic test before actual deployment 
The test framework works as follows  The system testers provide a set INLINEFORM0 of dialogues as input  Each dialogue INLINEFORM1 INLINEFORM2 INLINEFORM3 is an ordered set whose elements are represented by INLINEFORM4 , where INLINEFORM5 is the user utterance and INLINEFORM6 is an ordered set of pairs INLINEFORM7 that lists each response INLINEFORM8 each chatbot INLINEFORM9 should respond when the user says INLINEFORM10   For instance, Table UID98 shows a typical dialogue ( INLINEFORM11 ) between a user and the CognIA system  Note that we are omitting part of the expected answer with "   " just to better visualize the content of the table 
|p3 6cmp0 4cmp4 5cmp3 2cm|Content of dialogue INLINEFORM0 (example of dialogue in CognIA)Content of dialogue INLINEFORM1 (example of dialogue in CognIA
User utterance INLINEFORM0 rId Expected response INLINEFORM1 Chatbot INLINEFORM2
gray!25 hello 1 Hello Mediator
white what is cdb? 2 @CDBExpert what is cdb? Mediator
white 3 CDB is a type of investment that    CDB Expert
gray!25 which is better: cdb or savings account? 4 I found a post in the social media for     Mediator
white i would like to invest R$ 50 in six months 5 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 6 If you invest in Savings Account,     Savings Account Exp 
white 7 If you invest in CDB,    CDB Expert
white 8 Thanks Mediator
white 9 @User, there is no significant difference   Mediator
gray!25 so i want to invest R$ 10000 in 2 years 10 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
gray!25 11 If you invest in Savings Account,    Savings Account Exp 
gray!25 12 If you invest in CDB,    CDB Expert
gray!25 13 Thanks Mediator
gray!25 14 @User, in that case, it is better    Mediator
white what if i invest R$10,000 in 5 years? 15 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 16 If you invest in Saving Account,    Savings Account Exp 
white 17 If you invest in CDB,    CDB Expert
white 18 Thanks Mediator
white 19 @User, in that case, it is better    Mediator
gray!25 how about 15 years? 20 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
gray!25 21 If you invest in Savings Account,    Savings Account Exp
gray!25 22 If you invest in CDB,    CDB Expert
gray!25 23 Thanks Mediator
gray!25 24 @User, in that case, it is better    Mediator
white and 50,0000? 25 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 26 If you invest in Savings Account,    Savings Account Exp 
white 27 If you invest in CDB,    CDB Expert
white 28 Thanks Mediator
white 29 @User, in that case, it is better   Mediator
gray!25 I want to invest in 50,000 for 15 years in CDB 30 Sure, follow this link to your bank    Mediator
white thanks 31 You are welcome  Mediator
The testers may also inform the number of simulated users that will concurrently use the platform  Then, for each simulated user, the test framework iterates over the dialogues in INLINEFORM0 and iterates over the elements in each dialogue to check if each utterance INLINEFORM1 was correctly responded with INLINEFORM2 by the chatbot INLINEFORM3   There is a maximum time to wait  If a bot does not respond with the expected response in the maximum time (defined by the system developers), an error is raised and the test is stopped to inform the developers about the error  Otherwise, for each correct bot response, the test framework collects the time taken to respond that specific utterance by the bot for that specific user and continues for the next user utterance  Other consumption resource metrics (memory, CPU, network, disk)  The framework is divided into two parts  One part is responsible to gather resource consumption metrics and it resides inside SABIA  The other part works as clients (users) interacting with the server  It collects information about time taken to answer utterances and checks if the utterances are answered correctly 
By doing this, we not only provide a sanity test for the domain application (CognIA) developed in SABIA framework, but also a performance analysis of the platform  That is, we can: validate if the bots are answering correctly given a pre-defined set of known dialogues, check if they are answering in a reasonable time, and verify the amount of computing resources that were consumed to answer a specific utterance  Given the complexity of CognIA, these tests enable debugging of specific features like: understanding the amount of network bandwidth to use external services, or analyzing CPU and memory consumption when responding a specific utterance  The later may happen when the system is performing more complex calculations to indicate the investment return, for instance 
CognIA was deployed on IBM Bluemix, a platform as a service, on a Liberty for Java Cloud Foundry app with 3 GB RAM memory and 1 GB disk  Each of the modules shown in Figure FIGREF74 are deployed on separate Bluemix servers  Node JS and Socket IO servers are both deployed as Node Cloud Foundry apps, with 256 MB RAM memory and 512 MB disk each  Search Finance on Social Media is on a Go build pack Cloud Foundry app with 128 MB RAM memory and 128 GB disk  For the framework part that simulates clients, we instantiated a virtual machine with 8 cores on IBM's SoftLayer that is able to communicate with Bluemix  Then, the system testers built two dialogues, i e , INLINEFORM0   The example shown in Table UID98 is the dialogue test INLINEFORM1   For the dialogue INLINEFORM2 , although it also has 10 utterances, the testers varied some of them to check if other utterances in the finance domain (different from the ones in dialogue INLINEFORM3 ) are being responded as expected by the bots  Then, two tests are performed and the results are analyzed next  All tests were repeated until the standard deviation of the values was less than 1%  The results presented next are the average of these values within the 1% margin 
Test 1: The first test consists of running both dialogues INLINEFORM0 and INLINEFORM1 for only one user for sanity check  We set 30 seconds as the maximum time a simulated user should wait for a bot correct response before raising an error  The result is that all chatbots (Mediator, CDBExpert, and SavingsAccountExpert) responded all expected responses before the maximum time  Additionally, the framework collected how long each chatbot took to respond an expected answer 
In Figure FIGREF101 , we show the results for those time measurements for dialogue INLINEFORM0 , as for the dialogue INLINEFORM1 the results are approximately the same  The x-axis (Response Identifier) corresponds to the second column (Resp  Id) in Table UID98   We can see, for example, that when the bot CDBExpert responds with the message 3 to the user utterance "what is cdb?", it is the only bot that takes time different than zero to answer, which is the expected behavior  We can also see that the Mediator bot is the one that takes the longest, as it is responsible to coordinate the other bots and the entire dialogue with the user  Moreover, when the expert bots (CDBExpert and SavingsAccountExpert) are called by the Mediator to respond to the simulation calculations (this happens in responses 6, 7, 11, 12, 16, 17, 21, 22, 26, 27), they take approximately the same to respond  Finally, we see that when the concluding responses to the simulation calculations are given by the Mediator (this happens in responses 9, 14, 19, 24, 29), the response times reaches the greatest values, being 20 seconds the greatest value in response 19  These results support the system developers to understand the behavior of the system when simulated users interact with it and then focus on specific messages that are taking longer 
Test 2: This test consists of running dialogue INLINEFORM0 , but now using eight concurrent simulated users  We set the maximum time to wait to 240 seconds, i e , eight times the maximum set up for the single user in Test 1  The results are illustrated in Figure FIGREF102 , where we show the median time for the eight users  The maximum and minimum values are also presented with horizontal markers  Note that differently than what has been shown in Figure FIGREF101 , where each series represents one specific chatbot, in Figure FIGREF102 , the series represents the median response time for the responses in the order (x-axis) they are responded, regardless the chatbot 
Comparing the results in Figure FIGREF102 with the ones in Figure FIGREF101 , we can see that the bots take longer to respond when eight users are concurrently using the platform than when a single user uses it, as expected  For example, CDBExpert takes approximately 5 times longer to respond response 3 to eight users than to respond to one user  On average, the concluding responses to the simulation questions (i e , responses 9, 14, 19, 24, 29) take approximately 7 3 times more to be responded with eight users than with one user, being the response 9 the one that presented greatest difference (11 4 times longer with eight users than with one)  These results help the system developers to diagnose the scalability of the system architecture and to plan sizing and improvements 
Conclusions and Future Work
In this article, we explored the challenges of engineering MPCS and we have presented a hybrid conceptual architecture and its implementation with a finance advisory system 
We are currently evolving this architecture to be able to support decoupled interaction norms specification, and we are also developing a multi-party governance service that uses that specification to enforce exchange of compliant utterances 
In addition, we are exploring a micro-service implementation of SABIA in order to increase its scalability and performance, so thousands of members can join the system within thousands of conversations 
Acknowledgments
The authors would like to thank Maximilien de Bayser, Ana Paula Appel, Flavio Figueiredo and Marisa Vasconcellos, who contributed with discussions during SABIA and CognIA's implementation 
Context 3: Introduction
There exists a class of language construction known as pun in natural language texts and utterances, where a certain word or other lexical items are used to exploit two or more separate meanings  It has been shown that understanding of puns is an important research question with various real-world applications, such as human-computer interaction BIBREF0 , BIBREF1 and machine translation BIBREF2   Recently, many researchers show their interests in studying puns, like detecting pun sentences BIBREF3 , locating puns in the text BIBREF4 , interpreting pun sentences BIBREF5 and generating sentences containing puns BIBREF6 , BIBREF7 , BIBREF8   A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect  Puns can be generally categorized into two groups, namely heterographic puns (where the pun and its latent target are phonologically similar) and homographic puns (where the two meanings of the pun reflect its two distinct senses) BIBREF9   Consider the following two examples:
The first punning joke exploits the sound similarity between the word “propane" and the latent target “profane", which can be categorized into the group of heterographic puns  Another categorization of English puns is homographic pun, exemplified by the second instance leveraging distinct senses of the word “gut" 
Pun detection is the task of detecting whether there is a pun residing in the given text  The goal of pun location is to find the exact word appearing in the text that implies more than one meanings  Most previous work addresses such two tasks separately and develop separate systems BIBREF10 , BIBREF5   Typically, a system for pun detection is built to make a binary prediction on whether a sentence contains a pun or not, where all instances (with or without puns) are taken into account during training  For the task of pun location, a separate system is used to make a single prediction as to which word in the given sentence in the text that trigger more than one semantic interpretations of the text, where the training data involves only sentences that contain a pun  Therefore, if one is interested in solving both problems at the same time, a pipeline approach that performs pun detection followed by pun location can be used 
Compared to the pipeline methods, joint learning has been shown effective BIBREF11 , BIBREF12 since it is able to reduce error propagation and allows information exchange between tasks which is potentially beneficial to all the tasks  In this work, we demonstrate that the detection and location of puns can be jointly addressed by a single model  The pun detection and location tasks can be combined as a sequence labeling problem, which allows us to jointly detect and locate a pun in a sentence by assigning each word a tag  Since each context contains a maximum of one pun BIBREF9 , we design a novel tagging scheme to capture this structural constraint  Statistics on the corpora also show that a pun tends to appear in the second half of a context  To capture such a structural property, we also incorporate word position knowledge into our structured prediction model  Experiments on the benchmark datasets show that detection and location tasks can reinforce each other, leading to new state-of-the-art performance on these two tasks  To the best of our knowledge, this is the first work that performs joint detection and location of English puns by using a sequence labeling approach 
Problem Definition
We first design a simple tagging scheme consisting of two tags { INLINEFORM0 }:
INLINEFORM0 tag means the current word is not a pun 
INLINEFORM0 tag means the current word is a pun 
If the tag sequence of a sentence contains a INLINEFORM0 tag, then the text contains a pun and the word corresponding to INLINEFORM1 is the pun 
The contexts have the characteristic that each context contains a maximum of one pun BIBREF9   In other words, there exists only one pun if the given sentence is detected as the one containing a pun  Otherwise, there is no pun residing in the text  To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 } 
INLINEFORM0 tag indicates that the current word appears before the pun in the given context 
INLINEFORM0 tag highlights the current word is a pun 
INLINEFORM0 tag indicates that the current word appears after the pun 
We empirically show that the INLINEFORM0 scheme can guarantee the context property that there exists a maximum of one pun residing in the text 
Given a context from the training set, we will be able to generate its corresponding gold tag sequence using a deterministic procedure  Under the two schemes, if a sentence does not contain any puns, all words will be tagged with INLINEFORM0 or INLINEFORM1 , respectively  Exemplified by the second sentence “Some diets cause a gut reaction," the pun is given as “gut " Thus, under the INLINEFORM2 scheme, it should be tagged with INLINEFORM3 , while the words before it are assigned with the tag INLINEFORM4 and words after it are with INLINEFORM5 , as illustrated in Figure FIGREF8   Likewise, the INLINEFORM6 scheme tags the word “gut" with INLINEFORM7 , while other words are tagged with INLINEFORM8   Therefore, we can combine the pun detection and location tasks into one problem which can be solved by the sequence labeling approach 
Model
Neural models have shown their effectiveness on sequence labeling tasks BIBREF13 , BIBREF14 , BIBREF15   In this work, we adopt the bidirectional Long Short Term Memory (BiLSTM) BIBREF16 networks on top of the Conditional Random Fields BIBREF17 (CRF) architecture to make labeling decisions, which is one of the classical models for sequence labeling  Our model architecture is illustrated in Figure FIGREF8 with a running example  Given a context/sentence INLINEFORM0 where INLINEFORM1 is the length of the context, we generate the corresponding tag sequence INLINEFORM2 based on our designed tagging schemes and the original annotations for pun detection and location provided by the corpora  Our model is then trained on pairs of INLINEFORM3  
Input  The contexts in the pun corpus hold the property that each pun contains exactly one content word, which can be either a noun, a verb, an adjective, or an adverb  To capture this characteristic, we consider lexical features at the character level  Similar to the work of BIBREF15 , the character embeddings are trained by the character-level LSTM networks on the unannotated input sequences  Nonlinear transformations are then applied to the character embeddings by highway networks BIBREF18 , which map the character-level features into different semantic spaces 
We also observe that a pun tends to appear at the end of a sentence  Specifically, based on the statistics, we found that sentences with a pun that locate at the second half of the text account for around 88% and 92% in homographic and heterographic datasets, respectively  We thus introduce a binary feature that indicates if a word is located at the first or the second half of an input sentence to capture such positional information  A binary indicator can be mapped to a vector representation using a randomly initialized embedding table BIBREF19 , BIBREF20   In this work, we directly adopt the value of the binary indicator as part of the input 
The concatenation of the transformed character embeddings, the pre-trained word embeddings BIBREF21 , and the position indicators are taken as input of our model 
Tagging  The input is then fed into a BiLSTM network, which will be able to capture contextual information  For a training instance INLINEFORM0 , we suppose the output by the word-level BiLSTM is INLINEFORM1   The CRF layer is adopted to capture label dependencies and make final tagging decisions at each position, which has been included in many state-of-the-art sequence labeling models BIBREF14 , BIBREF15   The conditional probability is defined as:
where INLINEFORM0 is a set of all possible label sequences consisting of tags from INLINEFORM1 (or INLINEFORM2 ), INLINEFORM3 and INLINEFORM4 are weight and bias parameters corresponding to the label pair INLINEFORM5   During training, we minimize the negative log-likelihood summed over all training instances:
where INLINEFORM0 refers to the INLINEFORM1 -th instance in the training set  During testing, we aim to find the optimal label sequence for a new input INLINEFORM2 :
This search process can be done efficiently using the Viterbi algorithm 
Datasets and Settings
We evaluate our model on two benchmark datasets BIBREF9   The homographic dataset contains 2,250 contexts, 1,607 of which contain a pun  The heterographic dataset consists of 1,780 contexts with 1,271 containing a pun  We notice there is no standard splitting information provided for both datasets  Thus we apply 10-fold cross validation  To make direct comparisons with prior studies, following BIBREF4 , we accumulated the predictions for all ten folds and calculate the scores in the end 
For each fold, we randomly select 10% of the instances from the training set for development  Word embeddings are initialized with the 100-dimensional Glove BIBREF21   The dimension of character embeddings is 30 and they are randomly initialized, which can be fine tuned during training  The pre-trained word embeddings are not updated during training  The dimensions of hidden vectors for both char-level and word-level LSTM units are set to 300  We adopt stochastic gradient descent (SGD) BIBREF26 with a learning rate of 0

ground_truth: Custom dataset with user questions; set of documents, twitter posts and news articles, all related to finance.

a self-collected financial intents dataset in Portuguese

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Why masking words in the decoder is helpful?
Context 2: Introduction
Text summarization generates summaries from input documents while keeping salient information  It is an important task and can be applied to several real-world applications  Many methods have been proposed to solve the text summarization problem BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   There are two main text summarization techniques: extractive and abstractive  Extractive summarization generates summary by selecting salient sentences or phrases from the source text, while abstractive methods paraphrase and restructure sentences to compose the summary  We focus on abstractive summarization in this work as it is more flexible and thus can generate more diverse summaries 
Recently, many abstractive approaches are introduced based on neural sequence-to-sequence framework BIBREF4 , BIBREF0 , BIBREF3 , BIBREF5   Based on the sequence-to-sequence model with copy mechanism BIBREF6 , BIBREF0 incorporates a coverage vector to track and control attention scores on source text  BIBREF4 introduce intra-temporal attention processes in the encoder and decoder to address the repetition and incoherent problem 
There are two issues in previous abstractive methods: 1) these methods use left-context-only decoder, thus do not have complete context when predicting each word  2) they do not utilize the pre-trained contextualized language models on the decoder side, so it is more difficult for the decoder to learn summary representations, context interactions and language modeling together 
Recently, BERT has been successfully used in various natural language processing tasks, such as textual entailment, name entity recognition and machine reading comprehensions  In this paper, we present a novel natural language generation model based on pre-trained language models (we use BERT in this work)  As far as we know, this is the first work to extend BERT to the sequence generation task  To address the above issues of previous abstractive methods, in our model, we design a two-stage decoding process to make good use of BERT's context modeling ability  On the first stage, we generate the summary using a left-context-only-decoder  On the second stage, we mask each word of the summary and predict the refined word one-by-one using a refine decoder  To further improve the naturalness of the generated sequence, we cooperate reinforcement objective with the refine decoder 
The main contributions of this work are:
1  We propose a natural language generation model based on BERT, making good use of the pre-trained language model in the encoder and decoder process, and the model can be trained end-to-end without handcrafted features 
2  We design a two-stage decoder process  In this architecture, our model can generate each word of the summary considering both sides' context information 
3  We conduct experiments on the benchmark datasets CNN/Daily Mail and New York Times  Our model achieves a 33 33 average of ROUGE-1, ROUGE-2 and ROUGE-L on the CNN/Daily Mail, which is state-of-the-art  On the New York Times dataset, our model achieves about 5 6% relative improvement over ROUGE-1 
Text Summarization
In this paper, we focus on single-document multi-sentence summarization and propose a supervised abstractive model based on the neural attentive sequence-to-sequence framework which consists of two parts: a neural network for the encoder and another network for the decoder  The encoder encodes the input sequence to intermediate representation and the decoder predicts one word at a time step given the input sequence representation vector and previous decoded output  The goal of the model is to maximize the probability of generating the correct target sequences  In the encoding and generation process, the attention mechanism is used to concentrate on the most important positions of text  The learning objective of most sequence-to-sequence models is to minimize the negative log likelihood of the generated sequence as following equation shows, where $y^*_i$ is the i-th ground-truth summary token 
$$Loss = - \log \sum _{t=1}^N P(y_t^*|y_{<t}^*, X)$$   (Eq  3)
However, with this objective, traditional sequence generation models consider only one direction context in the decoding process, which could cause performance degradation since complete context of one token contains preceding and following tokens, thus feeding only preceded decoded words to the decoder so that the model may generate unnatural sequences  For example, attentive sequence-to-sequence models often generate sequences with repeated phrases which harm the naturalness  Some previous works mitigate this problem by improving the attention calculation process, but in this paper we show that feeding bi-directional context instead of left-only-context can better alleviate this problem 
Text summarization models are usually classified to abstractive and extractive ones  Recently, extractive models like DeepChannel BIBREF8 , rnn-ext+RL BIBREF9 and NeuSUM BIBREF2 achieve higher performances using well-designed structures  For example, DeepChannel propose a salience estimation network and iteratively extract salient sentences  BIBREF16 train a sentence compression model to teach another latent variable extractive model 
Also, several recent works focus on improving abstractive methods  BIBREF3 design a content selector to over-determine phrases in a source document that should be part of the summary  BIBREF11 introduce inconsistency loss to force words in less attended sentences(which determined by extractive model) to have lower generation probabilities  BIBREF5 extend seq2seq model with an information selection network to generate more informative summaries 
Bi-Directional Pre-Trained Context Encoders
Recently, context encoders such as ELMo, GPT, and BERT have been widely used in many NLP tasks  These models are pre-trained on a huge unlabeled corpus and can generate better contextualized token embeddings, thus the approaches built on top of them can achieve better performance 
Since our method is based on BERT, we illustrate the process briefly here  BERT consists of several layers  In each layer there is first a multi-head self-attention sub-layer and then a linear affine sub-layer with the residual connection  In each self-attention sub-layer the attention scores $e_{ij}$ are first calculated as Eq  ( 5 ) () shows, in which $d_e$ is output dimension, and $W^Q, W^K, W^V$ are parameter matrices 
$$&a_{ij} = \cfrac{(h_iW_Q)(h_jW_K)^T}{\sqrt{d_e}}  \\ &e_{ij} = \cfrac{\exp {e_{ij}}}{\sum _{k=1}^N\exp {e_{ik}}} $$   (Eq  5)
Then the output is calculated as Eq  ( 6 ) shows, which is the weighted sum of previous outputs $h$ added by previous output $h_i$   The last layer outputs is context encoding of input sequence 
$$o_i = h_i + \sum _{j=1}^{N} e_{ij}(h_j W_V) $$   (Eq  6)
Despite the wide usage and huge success, there is also a mismatch problem between these pre-trained context encoders and sequence-to-sequence models  The issue is that while using a pre-trained context encoder like GPT or BERT, they model token-level representations by conditioning on both direction context  During pre-training, they are fed with complete sequences  However, with a left-context-only decoder, these pre-trained language models will suffer from incomplete and inconsistent context and thus cannot generate good enough context-aware word representations, especially during the inference process 
Model
In this section, we describe the structure of our model, which learns to generate an abstractive multi-sentence summary from a given source document 
Based on the sequence-to-sequence framework built on top of BERT, we first design a refine decoder at word-level to tackle the two problems described in the above section  We also introduce a discrete objective for the refine decoders to reduce the exposure bias problem  The overall structure of our model is illustrated in Figure 1  
Problem Formulation
We denote the input document as $X = \lbrace x_1, \ldots , x_m\rbrace $ where $x_i \in \mathcal {X}$ represents one source token  The corresponding summary is denoted as $Y = \lbrace y_1, \ldots , y_L\rbrace $ , $L$ represents the summary length 
Given input document $X$ , we first predict the summary draft by a left-context-only decoder, and then using the generated summary draft we can condition on both context sides and refine the content of the summary  The draft will guide and constrain the refine process of summary 
Summary Draft Generation
The summary draft is based on the sequence-to-sequence model  On the encoder side the input document $X$ is encoded into representation vectors $H = \lbrace h_1, \ldots , h_m\rbrace $ , and then fed to the decoder to generate the summary draft $A = \lbrace a_1, \ldots , a_{|a|}\rbrace $  
We simply use BERT as the encoder  It first maps the input sequence to word embeddings and then computes document embeddings as the encoder's output, denoted by following equation 
$$H = BERT(x_1, \ldots , x_m)$$   (Eq  10)
In the draft decoder, we first introduce BERT's word embedding matrix to map the previous summary draft outputs $\lbrace y_1, \ldots , y_{t-1}\rbrace $ into embeddings vectors $\lbrace q_1, \ldots , q_{t-1}\rbrace $ at t-th time step  Note that as the input sequence of the decoder is not complete, we do not use the BERT network to predict the context vectors here 
Then we introduce an $N$ layer Transformer decoder to learn the conditional probability $P(A|H)$   Transformer's decoder-encoder multi-head attention helps the decoder learn soft alignments between summary and source document  At the t-th time step, the draft decoder predicts output probability conditioned on previous outputs and encoder hidden representations as Eq  ( 13 ) shows, in which $q_{<t} = \lbrace q_1, \ldots , q_{t-1}\rbrace $   Each generated sequence will be truncated in the first position of a special token '[PAD]' 
$$&P^{vocab}_t(w) = f_{dec}(q_{<t}, H)  \\ &L_{dec} = \sum _{i=1}^{|a|} -\log P(a_i = y_i^*|a_{< i}, H) $$   (Eq  13)
As Eq  () shows, the decoder's learning objective is to minimize negative likelihood of conditional probability, in which $y_i^*$ is the i-th ground truth word of summary 
However a decoder with this structure is not sufficient enough: if we use the BERT network in this decoder, then during training and inference, in-complete context(part of sentence) is fed into the BERT module, and although we can fine-tune BERT's parameters, the input distribution is quite different from the pre-train process, and thus harms the quality of generated context representations 
If we just use the embedding matrix here, it will be more difficult for the decoder with fresh parameters to learn to model representations as well as vocabulary probabilities, from a relative small corpus compared to BERT's huge pre-training corpus  In a word, the decoder cannot utilize BERT's ability to generate high quality context vectors, which will also harm performance 
This issue exists when using any other contextualized word representations, so we design a refine process to mitigate it in our approach which will be described in the next sub-section 
As some summary tokens are out-of-vocabulary words and occurs in input document, we incorporate copy mechanism BIBREF6 based on the Transformer decoder, we will describe it briefly 
At decoder time step $t$ , we first calculate the attention probability distribution over source document $X$ using the bi-linear dot product of the last layer decoder output of Transformer $o_t$ and the encoder output $h_j$ , as Eq  ( 15 ) () shows 
$$u_t^j =& o_t W_c h_j \\ \alpha _t^j =& \cfrac{\exp {u_t^j}}{\sum _{k=1}^N\exp {u_t^k}} $$   (Eq  15)
We then calculate copying gate $g_t\in [0, 1]$ , which makes a soft choice between selecting from source and generating from vocabulary, $W_c, W_g, b_g$ are parameters:
$$g_t = sigmoid(W_g \cdot [o_t, h] + b_g) $$   (Eq  16)
Using $g_t$ we calculate the weighted sum of copy probability and generation probability to get the final predicted probability of extended vocabulary $\mathcal {V} + \mathcal {X}$ , where $\mathcal {X}$ is the set of out of vocabulary words from the source document  The final probability is calculated as follow:
$$P_t(w) = (1-g_t)P_t^{vocab}(w) + g_t\sum _{i:w_i=w} \alpha _t^i$$   (Eq  17)
Summary Refine Process
The main reason to introduce the refine process is to enhance the decoder using BERT's contextualized representations, so we do not modify the encoder and reuse it during this process 
On the decoder side, we propose a new word-level refine decoder  The refine decoder receives a generated summary draft as input, and outputs a refined summary  It first masks each word in the summary draft one by one, then feeds the draft to BERT to generate context vectors  Finally it predicts a refined summary word using an $N$ layer Transformer decoder which is the same as the draft decoder  At t-th time step the n-th word of input summary is masked, and the decoder predicts the n-th refined word given other words of the summary 
The learning objective of this process is shown in Eq  ( 19 ), $y_i$ is the i-th summary word and $y_{i}^*$ for the ground-truth summary word, and $a_{\ne i} = \lbrace a_1, \ldots , a_{i-1}, a_{i+1}, \ldots , a_{|y|}\rbrace $  
$$L_{refine} = \sum _{i=1}^{|y|} -\log P(y_i = y_i^*|a_{\ne i}, H) $$   (Eq  19)
From the view of BERT or other contextualized embeddings, the refine decoding process provides a more complete input sequence which is consistent with their pre-training processes  Intuitively, this process works as follows: first the draft decoder writes a summary draft based on a document, and then the refine decoder edits the draft  It concentrates on one word at a time, based on the source document as well as other words 
We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences 
The parameters are shared between the draft decoder and refine decoder, as we find that using individual parameters the model's performance degrades a lot  The reason may be that we use teach-forcing during training, and thus the word-level refine decoder learns to predict words given all the other ground-truth words of summary  This objective is similar to the language model's pre-train objective, and is probably not enough for the decoder to learn to generate refined summaries  So in our model all decoders share the same parameters 
Researchers usually use ROUGE as the evaluation metric for summarization, however during sequence-to-sequence model training, the objective is to maximize the log likelihood of generated sequences  This mis-match harms the model's performance, so we add a discrete objective to the model, and optimize it by introducing the policy gradient method  For example, the discrete objective for the summary draft process is as Eq  ( 21 ) shows, where $a^s$ is the draft summary sampled from predicted distribution, and $R(a^s)$ is the reward score compared with the ground-truth summary, we use ROUGE-L in our experiment  To balance between optimizing the discrete objective and generating readable sequences, we mix the discrete objective with maximum-likelihood objective  As Eq  () shows, minimizing $\hat{L}_{dec}$ is the final objective for the draft process, note here $L_{dec}$ is $-logP(a|x)$   In the refine process we introduce similar objectives 
$$L^{rl}_{dec} = R(a^s)\cdot [-\log (P(a^s|x))]  \\ \hat{L}_{dec} = \gamma * L^{rl}_{dec} + (1 - \gamma ) * L_{dec} $$   (Eq  21)
Learning and Inference
During model training, the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm  During training we feed the ground-truth summary to each decoder and minimize the objective 
$$L_{model} = \hat{L}_{dec} + \hat{L}_{refine}$$   (Eq  23)
At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries 
Settings
In this work, all of our models are built on $BERT_{BASE}$ , although another larger pre-trained model with better performance ( $BERT_{LARGE}$ ) has published but it costs too much time and GPU memory  We use WordPiece embeddings with a 30,000 vocabulary which is the same as BERT  We set the layer of transformer decoders to 12(8 on NYT50), and set the attention heads number to 12(8 on NYT50), set fully-connected sub-layer hidden size to 3072  We train the model using an Adam optimizer with learning rate of $3e-4$ , $\beta _1=0 9$ , $\beta _2=0 999$ and $\epsilon =10^{-9}$ and use a dynamic learning rate during the training process  For regularization, we use dropout BIBREF13 and label smoothing BIBREF14 in our models and set the dropout rate to 0 15, and the label smoothing value to 0 1  We set the RL objective factor $\gamma $ to 0 99 
During training, we set the batch size to 36, and train for 4 epochs(8 epochs for NYT50 since it has many fewer training samples), after training the best model are selected from last 10 models based on development set performance  Due to GPU memory limit, we use gradient accumulation, set accumulate step to 12 and feed 3 samples at each step  We use beam size 4 and length penalty of 1 0 to generate logical form sequences 
We filter repeated tri-grams in beam-search process by setting word probability to zero if it will generate an tri-gram which exists in the existing summary  It is a nice method to avoid phrase repetition since the two datasets seldom contains repeated tri-grams in one summary  We also fine tune the generated sequences using another two simple rules  When there are multi summary sentences with exactly the same content, we keep the first one and remove the other sentences; we also remove sentences with less than 3 words from the result 
To evaluate the performance of our model, we conduct experiments on CNN/Daily Mail dataset, which is a large collection of news articles and modified for summarization  Following BIBREF0 we choose the non-anonymized version of the dataset, which consists of more than 280,000 training samples and 11490 test set samples 
We also conduct experiments on the New York Times(NYT) dataset which also consists of many news articles  The original dataset can be applied here  In our experiment, we follow the dataset splits and other pre-process settings of BIBREF15   We first filter all samples without a full article text or abstract and then remove all samples with summaries shorter than 50 words  Then we choose the test set based on the date of publication(all examples published after January 1, 2007)  The final dataset contains 22,000 training samples and 3,452 test samples and is called NYT50 since all summaries are longer than 50 words 
We tokenize all sequences of the two datasets using the WordPiece tokenizer  After tokenizing, the average article length and summary length of CNN/Daily Mail are 691 and 51, and NYT50's average article length and summary length are 1152 and 75  We truncate the article length to 512, and the summary length to 100 in our experiment(max summary length is set to 150 on NYT50 as its average golden summary length is longer) 
On CNN/Daily Mail dataset, we report the full-length F-1 score of the ROUGE-1, ROUGE-2 and ROUGE-L metrics, calculated using PyRouge package and the Porter stemmer option  On NYT50, following BIBREF4 we evaluate limited length ROUGE recall score(limit the generated summary length to the ground truth length)  We split NYT50 summaries into sentences by semicolons to calculate the ROUGE scores 
Results and Analysis
Table 1 shows the results on CNN/Daily Mail dataset, we compare the performance of many recent approaches with our model  We classify them to two groups based on whether they are extractive or abstractive models  As the last line of the table shows, the ROUGE-1 and ROUGE-2 score of our full model is comparable with DCA, and outperforms on ROUGE-L  Also, compared to extractive models NeuSUM and MASK- $LM^{global}$ , we achieve slight higher ROUGE-1  Except the four scores, our model outperforms these models on all the other scores, and since we have 95% confidence interval of at most $\pm $ 0 20, these improvements are statistically significant 
As the last four lines of Table 1 show, we conduct an ablation study on our model variants to analyze the importance of each component  We use three ablation models for the experiments  One-Stage: A sequence-to-sequence model with copy mechanism based on BERT; Two-Stage: Adding the word-refine decoder to the One-Stage model; Two-Stage + RL: Full model with refine process cooperated with RL objective 
First, we compare the Two-Stage+RL model with Two-Stage ablation, we observe that the full model outperforms by 0 30 on average ROUGE, suggesting that the reinforcement objective helps the model effectively  Then we analyze the effect of refine process by removing word-level refine from the Two-Stage model, we observe that without the refine process the average ROUGE score drops by 1 69  The ablation study shows that each module is necessary for our full model, and the improvements are statistically significant on all metrics 
To evaluate the impact of summary length on model performance, we compare the average rouge score improvements of our model with different length of ground-truth summaries  As the above sub-figure of Figure 2 shows, compared to Pointer-Generator with Coverage, on length interval 40-80(occupies about 70% of test set) the improvements of our model are higher than shorter samples, confirms that with better context representations, in longer documents our model can achieve higher performance 
As the below sub-figure of Figure 2 shows, compared to extractive baseline: Lead-3 BIBREF0 , the advantage of our model will fall when golden summary length is greater than 80  This probably because that we truncate the long documents and golden summaries and cannot get full information, it could also because that the training data in these intervals is too few to train an abstractive model, so simple extractive method will not fall too far behind 
Additional Results on NYT50
Table 2 shows experiments on the NYT50 corpus  Since the short summary samples are filtered, NYT50 has average longer summaries than CNN/Daily Mail  So the model needs to catch long-term dependency of the sequences to generate good summaries 
The first two lines of Table 2 show results of the two baselines introduced by BIBREF15 : these baselines select first n sentences, or select the first k words from the original document  Also we compare performance of our model with two recent models, we see 2 39 ROUGE-1 improvements compared to the ML+RL with intra-attn approach(previous SOTA) carries over to this dataset, which is a large margin  On ROUGE-2, our model also get an improvement of 0 51  The experiment proves that our approach can outperform competitive methods on different data distributions 
Pre-trained language models
Pre-trained word vectors BIBREF17 , BIBREF18 , BIBREF19 have been widely used in many NLP tasks  More recently, pre-trained language models (ELMo, GPT and BERT), have also achieved great success on several NLP problems such as textual entailment, semantic similarity, reading comprehension, and question answering BIBREF20 , BIBREF21 , BIBREF22  
Some recent works also focus on leveraging pre-trained language models in summarization  BIBREF23 pretrain a language model and use it as the sentiment analyser when generating reviews of goods  BIBREF24 train a language model on golden summaries, and then use it on the decoder side to incorporate prior knowledge 
In this work, we use BERT(which is a pre-trained language model using large scale unlabeled data) on the encoder and decoder of a seq2seq model, and by designing a two stage decoding structure we build a competitive model for abstractive text summarization 
Conclusion and Future Work
In this work, we propose a two-stage model based on sequence-to-sequence paradigm  Our model utilize BERT on both encoder and decoder sides, and introduce reinforce objective in learning process  We evaluate our model on two benchmark datasets CNN/Daily Mail and New York Times, the experimental results show that compared to previous systems our approach effectively improves performance 
Although our experiments are conducted on summarization task, our model can be used in most natural language generation tasks, such as machine translation, question generation and paraphrasing  The refine decoder and mixed objective can also be applied on other sequence generation tasks, and we will investigate on them in future work 

ground_truth: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What is the tagging scheme employed?
Context 3: 015 
For the pun detection task, if the predicted tag sequence contains at least one INLINEFORM0 tag, we regard the output (i e , the prediction of our pun detection model) for this task as true, otherwise false  For the pun location task, a predicted pun is regarded as correct if and only if it is labeled as the gold pun in the dataset  As to pun location, to make fair comparisons with prior studies, we only consider the instances that are labeled as the ones containing a pun  We report precision, recall and INLINEFORM1 score in Table TABREF11   A list of prior works that did not employ joint learning are also shown in the first block of Table TABREF11  
Results
We also implemented a baseline model based on conditional random fields (CRF), where features like POS tags produced by the Stanford POS tagger BIBREF27 , n-grams, label transitions, word suffixes and relative position to the end of the text are considered  We can see that our model with the INLINEFORM0 tagging scheme yields new state-of-the-art INLINEFORM1 scores on pun detection and competitive results on pun location, compared to baselines that do not adopt joint learning in the first block  For location on heterographic puns, our model's performance is slightly lower than the system of BIBREF25 , which is a rule-based locator  Compared to CRF, we can see that our model, either with the INLINEFORM2 or the INLINEFORM3 scheme, yields significantly higher recall on both detection and location tasks, while the precisions are relatively close  This demonstrates the effectiveness of BiLSTM, which learns the contextual features of given texts – such information appears to be helpful in recalling more puns 
Compared to the INLINEFORM0 scheme, the INLINEFORM1 tagging scheme is able to yield better performance on these two tasks  After studying outputs from these two approaches, we found that one leading source of error for the INLINEFORM2 approach is that there exist more than one words in a single instance that are assigned with the INLINEFORM3 tag  However, according to the description of pun in BIBREF9 , each context contains a maximum of one pun  Thus, such a useful structural constraint is not well captured by the simple approach based on the INLINEFORM4 tagging scheme  On the other hand, by applying the INLINEFORM5 tagging scheme, such a constraint is properly captured in the model  As a result, the results for such a approach are significantly better than the approach based on the INLINEFORM6 tagging scheme, as we can observe from the table  Under the same experimental setup, we also attempted to exclude word position features  Results are given by INLINEFORM7 - INLINEFORM8   It is expected that the performance of pun location drops, since such position features are able to capture the interesting property that a pun tends to appear in the second half of a sentence  While such knowledge is helpful for the location task, interestingly, a model without position knowledge yields improved performance on the pun detection task  One possible reason is that detecting whether a sentence contains a pun is not concerned with such word position information 
Additionally, we conduct experiments over sentences containing a pun only, namely 1,607 and 1,271 instances from homographic and heterographic pun corpora separately  It can be regarded as a “pipeline” method where the classifier for pun detection is regarded as perfect  Following the prior work of BIBREF4 , we apply 10-fold cross validation  Since we are given that all input sentences contain a pun, we only report accumulated results on pun location, denoted as Pipeline in Table TABREF11   Compared with our approaches, the performance of such an approach drops significantly  On the other hand, such a fact demonstrates that the two task, detection and location of puns, can reinforce each other  These figures demonstrate the effectiveness of our sequence labeling method to detect and locate English puns in a joint manner 
Error Analysis
We studied the outputs from our system and make some error analysis  We found the errors can be broadly categorized into several types, and we elaborate them here  1) Low word coverage: since the corpora are relatively small, there exist many unseen words in the test set  Learning the representations of such unseen words is challenging, which affects the model's performance  Such errors contribute around 40% of the total errors made by our system  2) Detection errors: we found many errors are due to the model's inability to make correct pun detection  Such inability harms both pun detection and pun location  Although our approach based on the INLINEFORM0 tagging scheme yields relatively higher scores on the detection task, we still found that 40% of the incorrectly predicted instances fall into this group  3) Short sentences: we found it was challenging for our model to make correct predictions when the given text is short  Consider the example “Superglue! Tom rejoined," here the word rejoined is the corresponding pun  However, it would be challenging to figure out the pun with such limited contextual information 
Related Work
Most existing systems address pun detection and location separately  BIBREF22 applied word sense knowledge to conduct pun detection  BIBREF24 trained a bidirectional RNN classifier for detecting homographic puns  Next, a knowledge-based approach is adopted to find the exact pun  Such a system is not applicable to heterographic puns  BIBREF28 applied Google n-gram and word2vec to make decisions  The phonetic distance via the CMU Pronouncing Dictionary is computed to detect heterographic puns  BIBREF10 used the hidden Markov model and a cyclic dependency network with rich features to detect and locate puns  BIBREF23 used a supervised approach to pun detection and a weakly supervised approach to pun location based on the position within the context and part of speech features  BIBREF25 proposed a rule-based system for pun location that scores candidate words according to eleven simple heuristics  Two systems are developed to conduct detection and location separately in the system known as UWAV BIBREF3   The pun detector combines predictions from three classifiers  The pun locator considers word2vec similarity between every pair of words in the context and position to pinpoint the pun  The state-of-the-art system for homographic pun location is a neural method BIBREF4 , where the word senses are incorporated into a bidirectional LSTM model  This method only supports the pun location task on homographic puns  Another line of research efforts related to this work is sequence labeling, such as POS tagging, chunking, word segmentation and NER  The neural methods have shown their effectiveness in this task, such as BiLSTM-CNN BIBREF13 , GRNN BIBREF29 , LSTM-CRF BIBREF30 , LSTM-CNN-CRF BIBREF14 , LM-LSTM-CRF BIBREF15  
In this work, we combine pun detection and location tasks as a single sequence labeling problem  Inspired by the work of BIBREF15 , we also adopt a LSTM-CRF with character embeddings to make labeling decisions 
Conclusion
In this paper, we propose to perform pun detection and location tasks in a joint manner from a sequence labeling perspective  We observe that each text in our corpora contains a maximum of one pun  Hence, we design a novel tagging scheme to incorporate such a constraint  Such a scheme guarantees that there is a maximum of one word that will be tagged as a pun during the testing phase  We also found the interesting structural property such as the fact that most puns tend to appear at the second half of the sentences can be helpful for such a task, but was not explored in previous works  Furthermore, unlike many previous approaches, our approach, though simple, is generally applicable to both heterographic and homographic puns  Empirical results on the benchmark datasets prove the effectiveness of the proposed approach that the two tasks of pun detection and location can be addressed by a single model from a sequence labeling perspective 
Future research includes the investigations on how to make use of richer semantic and linguistic information for detection and location of puns  Research on puns for other languages such as Chinese is still under-explored, which could also be an interesting direction for our future studies 
Acknowledgments
We would like to thank the three anonymous reviewers for their thoughtful and constructive comments  This work is supported by Singapore Ministry of Education Academic Research Fund (AcRF) Tier 2 Project MOE2017-T2-1-156, and is partially supported by SUTD project PIE-SGP-AI-2018-01 

ground_truth: A new tagging scheme that tags the words before and after the pun as well as the pun words.

a new tagging scheme consisting of three tags, namely { INLINEFORM0 }