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 metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? 
Context 1: Introduction
Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing  Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   Recent neural conversation models BIBREF4 , BIBREF5 , BIBREF6 are even able to perform open-ended conversations  However, since they do not use explicit knowledge bases and do not perform inference, they often suffer from generic and dull responses BIBREF5 , BIBREF7   More recently, BIBREF8 and BIBREF9 proposed to use knowledge bases (KBs) to help generate responses for knowledge-grounded conversation  However, one major weakness of all existing chat systems is that they do not explicitly or implicitly learn new knowledge in the conversation process  This seriously limits the scope of their applications  In contrast, we humans constantly learn new knowledge in our conversations  Even if some existing systems can use very large knowledge bases either harvested from a large data source such as the Web or built manually, these KBs still miss a large number of facts (knowledge) BIBREF10   It is thus important for a chatbot to continuously learn new knowledge in the conversation process to expand its KB and to improve its conversation ability 
In recent years, researchers have studied the problem of KB completion, i e , inferring new facts (knowledge) automatically from existing facts in a KB  KB completion (KBC) is defined as a binary classification problem: Given a query triple, ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we want to predict whether the source entity INLINEFORM3 and target entity INLINEFORM4 can be linked by the relation INLINEFORM5   However, existing approaches BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 solve this problem under the closed-world assumption, i e , INLINEFORM6 , INLINEFORM7 and INLINEFORM8 are all known to exist in the KB  This is a major weakness because it means that no new knowledge or facts may contain unknown entities or relations  Due to this limitation, KBC is clearly not sufficient for knowledge learning in conversations because in a conversation, the user can say anything, which may contain entities and relations that are not already in the KB 
In this paper, we remove this assumption of KBC, and allow all INLINEFORM0 , INLINEFORM1 and INLINEFORM2 to be unknown  We call the new problem open-world knowledge base completion (OKBC)  OKBC generalizes KBC  Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations  In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting 
From the perspective of knowledge learning in conversations, essentially we can extract two key types of information, true facts and queries, from the user utterances  Queries are facts whose truth values need to be determined  Note that we do not study fact or relation extraction in this paper as there is an extensive work on the topic  (1) For a true fact, we will incorporate it into the KB  Here we need to make sure that it is not already in the KB, which involves relation resolution and entity linking  After a fact is added to the KB, we may predict that some related facts involving some existing relations in the KB may also be true (not logical implications as they can be automatically inferred)  For example, if the user says “Obama was born in USA,” the system may guess that (Obama, CitizenOf, USA) (meaning that Obama is a citizen of USA) could also be true based on the current KB  To verify this fact, it needs to solve a KBC problem by treating (Obama, CitizenOf, USA) as a query  This is a KBC problem because the fact (Obama, BornIn, USA) extracted from the original sentence has been added to the KB  Then Obama and USA are in the KB  If the KBC problem is solved, it learns a new fact (Obama, CitizenOf, USA) in addition to the extracted fact (Obama, BornIn, USA)  (2) For a query fact, e g , (Obama, BornIn, USA) extracted from the user question “Was Obama born in USA?” we need to solve the OKBC problem if any of “Obama, “BornIn”, or “USA" is not already in the KB 
We can see that OKBC is the core of a knowledge learning engine for conversation  Thus, in this paper, we focus on solving it  We assume that other tasks such as fact/relation extraction and resolution and guessing of related facts of an extracted fact are solved by other sub-systems 
We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation  Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge  If our knowledge does not allow us to draw a conclusion, we typically ask questions to others to acquire related knowledge and use it in inference  The process typically involves an inference strategy (a sequence of actions), which interleaves a sequence of processing and interactive actions  A processing action can be the selection of related facts, deriving inference chain, etc , that advances the inference process  An interactive action can be deciding what to ask, formulating a suitable question, etc , that enable us to interact  The process helps grow the knowledge over time and the gained knowledge enables us to communicate better in the future  We call this lifelong interactive learning and inference (LiLi)  Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning  LiLi should have the following capabilities:
This setting is ideal for many NLP applications like dialog and question-answering systems that naturally provide the scope for human interaction and demand real-time inference 
LiLi starts with the closed-world KBC approach path-ranking (PR) BIBREF11 , BIBREF17 and extends KBC in a major way to open-world knowledge base completion (OKBC)  For a relation INLINEFORM0 , PR works by enumerating paths (except single-link path INLINEFORM1 ) between entity-pairs linked by INLINEFORM2 in the KB and use them as features to train a binary classifier to predict whether a query INLINEFORM3 should be in the KB  Here, a path between two entities is a sequence of relations linking them  In our work, we adopt the latest PR method, C-PR BIBREF16 and extend it to make it work in the open-world setting  C-PR enumerates paths by performing bidirectional random walks over the KB graph while leveraging the context of the source-target entity-pair  We also adopt and extend the compositional vector space model BIBREF20 , BIBREF21 with continual learning capability for prediction 
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (e g , “(Obama, CitizenOf, USA), which means whether Obama a citizen of USA), LiLi interacts with the user (if needed) by dynamically formulating questions (see the interaction example in Figure 1, which will be further explained in §3) and leverages the interactively acquired knowledge (supporting facts (SFs) in the figure) for continued inference  To do so, LiLi formulates a query-specific inference strategy and executes it  We design LiLi in a Reinforcement Learning (RL) setting that performs sub-tasks like formulating and executing strategy, training a prediction model for inference, and knowledge retention for future use  To the best of our knowledge, our work is the first to address the OKBC problem and to propose an interactive learning mechanism to solve it in a continuous or lifelong manner  We empirically verify the effectiveness of LiLi on two standard real-world KBs: Freebase and WordNet  Experimental results show that LiLi is highly effective in terms of its predictive performance and strategy formulation ability 
Related Work
To the best of our knowledge, we are not aware of any knowledge learning system that can learn new knowledge in the conversation process  This section thus discusses other related work 
Among existing KB completion approaches, BIBREF20 extended the vector space model for zero-shot KB inference  However, the model cannot handle unknown entities and can only work on fixed set of unknown relations with known embeddings  Recently, BIBREF22 proposed a method using external text corpus to perform inference on unknown entities  However, the method cannot handle unknown relations  Thus, these methods are not suitable for our open-world setting  None of the existing KB inference methods perform interactive knowledge learning like LiLi  NELL BIBREF23 continuously updates its KB using facts extracted from the Web  Our task is very different as we do not do Web fact extraction (which is also useful)  We focus on user interactions in this paper  Our work is related to interactive language learning (ILL) BIBREF24 , BIBREF25 , but these are not about KB completion  The work in BIBREF26 allows a learner to ask questions in dialogue  However, this work used RL to learn about whether to ask the user or not  The “what to ask aspect" was manually designed by modeling synthetic tasks  LiLi formulates query-specific inference strategies which embed interaction behaviors  Also, no existing dialogue systems BIBREF4 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 employ lifelong learning to train prediction models by using information/knowledge retained in the past 
Our work is related to general lifelong learning in BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36   However, they learn only one type of tasks, e g , supervised, topic modeling or reinforcement learning (RL) tasks  None of them is suitable for our setting, which involves interleaving of RL, supervised and interactive learning  More details about lifelong learning can be found in the book BIBREF31  
Interactive Knowledge Learning (LiLi)
We design LiLi as a combination of two interconnected models: (1) a RL model that learns to formulate a query-specific inference strategy for performing the OKBC task, and (2) a lifelong prediction model to predict whether a triple should be in the KB, which is invoked by an action while executing the inference strategy and is learned for each relation as in C-PR  The framework improves its performance over time through user interaction and knowledge retention  Compared to the existing KB inference methods, LiLi overcomes the following three challenges for OKBC:
1  Mapping open-world to close-world  Being a closed-world method, C-PR cannot extract path features and learn a prediction model when any of INLINEFORM0 , INLINEFORM1 or INLINEFORM2 is unknown  LiLi solves this problem through interactive knowledge acquisition  If INLINEFORM3 is unknown, LiLi asks the user to provide a clue (an example of INLINEFORM4 )  And if INLINEFORM5 or INLINEFORM6 is unknown, LiLi asks the user to provide a link (relation) to connect the unknown entity with an existing entity (automatically selected) in the KB  We refer to such a query as a connecting link query (CLQ)  The acquired knowledge reduces OKBC to KBC and makes the inference task feasible 
2  Spareseness of KB  A main issue of all PR methods like C-PR is the connectivity of the KB graph  If there is no path connecting INLINEFORM0 and INLINEFORM1 in the graph, path enumeration of C-PR gets stuck and inference becomes infeasible  In such cases, LiLi uses a template relation (“@-?-@") as the missing link marker to connect entity-pairs and continues feature extraction  A path containing “@-?-@" is called an incomplete path  Thus, the extracted feature set contains both complete (no missing link) and incomplete paths  Next, LiLi selects an incomplete path from the feature set and asks the user to provide a link for path completion  We refer to such a query as missing link query (MLQ) 
3  Limitation in user knowledge  If the user is unable to respond to MLQs or CLQs, LiLi uses a guessing mechanism (discussed later) to fill the gap  This enables LiLi to continue its inference even if the user cannot answer a system question 
Components of LiLi
As lifelong learning needs to retain knowledge learned from past tasks and use it to help future learning BIBREF31 , LiLi uses a Knowledge Store (KS) for knowledge retention  KS has four components: (i) Knowledge Graph ( INLINEFORM0 ): INLINEFORM1 (the KB) is initialized with base KB triples (see §4) and gets updated over time with the acquired knowledge  (ii) Relation-Entity Matrix ( INLINEFORM2 ): INLINEFORM3 is a sparse matrix, with rows as relations and columns as entity-pairs and is used by the prediction model  Given a triple ( INLINEFORM4 , INLINEFORM5 , INLINEFORM6 ) INLINEFORM7 , we set INLINEFORM8 [ INLINEFORM9 , ( INLINEFORM10 , INLINEFORM11 )] = 1 indicating INLINEFORM12 occurs for pair ( INLINEFORM13 , INLINEFORM14 )  (iii) Task Experience Store ( INLINEFORM15 ): INLINEFORM16 stores the predictive performance of LiLi on past learned tasks in terms of Matthews correlation coefficient (MCC) that measures the quality of binary classification  So, for two tasks INLINEFORM17 and INLINEFORM18 (each relation is a task), if INLINEFORM19 [ INLINEFORM20 ] INLINEFORM21 INLINEFORM22 [ INLINEFORM23 ] [where INLINEFORM24 [ INLINEFORM25 ]=MCC( INLINEFORM26 )], we say C-PR has learned INLINEFORM27 well compared to INLINEFORM28   (iv) Incomplete Feature DB ( INLINEFORM29 ): INLINEFORM30 stores the frequency of an incomplete path INLINEFORM31 in the form of a tuple ( INLINEFORM32 , INLINEFORM33 , INLINEFORM34 ) and is used in formulating MLQs  INLINEFORM35 [( INLINEFORM36 , INLINEFORM37 , INLINEFORM38 )] = INLINEFORM39 implies LiLi has extracted incomplete path INLINEFORM40 INLINEFORM41 times involving entity-pair INLINEFORM42 [( INLINEFORM43 , INLINEFORM44 )] for query relation INLINEFORM45  
The RL model learns even after training whenever it encounters an unseen state (in testing) and thus, gets updated over time  KS is updated continuously over time as a result of the execution of LiLi and takes part in future learning  The prediction model uses lifelong learning (LL), where we transfer knowledge (parameter values) from the model for a past most similar task to help learn for the current task  Similar tasks are identified by factorizing INLINEFORM0 and computing a task similarity matrix INLINEFORM1   Besides LL, LiLi uses INLINEFORM2 to identify poorly learned past tasks and acquire more clues for them to improve its skillset over time 
LiLi also uses a stack, called Inference Stack ( INLINEFORM0 ) to hold query and its state information for RL  LiLi always processes stack top ( INLINEFORM1 [top])  The clues from the user get stored in INLINEFORM2 on top of the query during strategy execution and processed first  Thus, the prediction model for INLINEFORM3 is learned before performing inference on query, transforming OKBC to a KBC problem  Table 1 shows the parameters of LiLi used in the following sections 
Working of LiLi
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we represent it as a data instance INLINEFORM3   INLINEFORM4 consists of INLINEFORM5 (the query triple), INLINEFORM6 (interaction limit set for INLINEFORM7 ), INLINEFORM8 (experience list storing the transition history of MDP for INLINEFORM9 in RL) and INLINEFORM10 (mode of INLINEFORM11 ) denoting if INLINEFORM12 is ` INLINEFORM13 ' (training), ` INLINEFORM14 ' (validation), ` INLINEFORM15 ' (evaluation) or ` INLINEFORM16 ' (clue) instance and INLINEFORM17 (feature set)  We denote INLINEFORM18 ( INLINEFORM19 ) as the set of all complete (incomplete) path features in INLINEFORM20   Given a data instance INLINEFORM21 , LiLi starts its initialization as follows: it sets the state as INLINEFORM22 (based on INLINEFORM23 , explained later), pushes the query tuple ( INLINEFORM24 , INLINEFORM25 ) into INLINEFORM26 and feeds INLINEFORM27 [top] to the RL-model for strategy formulation from INLINEFORM28  
Inference Strategy Formulation  We view solving the strategy formulation problem as learning to play an inference game, where the goal is to formulate a strategy that "makes the inference task possible"  Considering PR methods, inference is possible, iff (1) INLINEFORM0 becomes known to its KB (by acquiring clues when INLINEFORM1 is unknown) and (2) path features are extracted between INLINEFORM2 and INLINEFORM3 (which inturn requires INLINEFORM4 and INLINEFORM5 to be known to KB)  If these conditions are met at the end of an episode (when strategy formulation finishes for a given query) of the game, LiLi wins and thus, it trains the prediction model for INLINEFORM6 and uses it for inference 
LiLi's strategy formulation is modeled as a Markov Decision Process (MDP) with finite state ( INLINEFORM0 ) and action ( INLINEFORM1 ) spaces  A state INLINEFORM2 consists of 10 binary state variables (Table 2), each of which keeps track of results of an action INLINEFORM3 taken by LiLi and thus, records the progress in inference process made so far  INLINEFORM4 is the initial state with all state bits set as 0  If the data instance (query) is a clue [ INLINEFORM5 ], INLINEFORM6 [CLUE] is set as 1  INLINEFORM7 consists of 6 actions (Table 3)  INLINEFORM8 , INLINEFORM9 , INLINEFORM10 are processing actions and INLINEFORM11 , INLINEFORM12 , INLINEFORM13 are interactive actions  Whenever INLINEFORM14 is executed, the MDP reaches the terminal state  Given an action INLINEFORM15 in state INLINEFORM16 , if INLINEFORM17 is invalid in INLINEFORM21 or the objective of INLINEFORM22 is unsatisfied (* marked the condition in INLINEFORM23 ), RL receives a negative reward (empirically set); else receives a positive reward   We use Q-learning BIBREF38 with INLINEFORM24 -greedy strategy to learn the optimal policy for training the RL model  Note that, the inference strategy is independent of KB type and correctness of prediction  Thus, the RL-model is trained only once from scratch (reused thereafter for other KBs) and also, independently of the prediction model 
Sometimes the training dataset may not be enough to learn optimal policy for all INLINEFORM0   Thus, encountering an unseen state during test can make RL-model clueless about the action  Given a state INLINEFORM1 , whenever an invalid INLINEFORM2 is chosen, LiLi remains in INLINEFORM3   For INLINEFORM4 , LiLi remains in INLINEFORM5 untill INLINEFORM6 (see Table 1 for INLINEFORM7 )  So, if the state remains the same for ( INLINEFORM8 +1) times, it implies LiLi has encountered a fault (an unseen state)  RL-model instantly switches to the training mode and randomly explores INLINEFORM9 to learn the optimal action (fault-tolerant learning)  While exploring INLINEFORM10 , the model chooses INLINEFORM11 only when it has tried all other INLINEFORM12 to avoid abrupt end of episode 
Execution of Actions  At any given point in time, let ( INLINEFORM0 , INLINEFORM1 ) be the current INLINEFORM2 [top], INLINEFORM3 is the chosen action and the current version of KS components are INLINEFORM4 , INLINEFORM5 , INLINEFORM6 and INLINEFORM7   Then, if INLINEFORM8 is invalid in INLINEFORM9 , LiLi only updates INLINEFORM10 [top] with ( INLINEFORM11 , INLINEFORM12 ) and returns INLINEFORM13 [top] to RL-model  In this process, LiLi adds experience ( INLINEFORM14 , INLINEFORM15 , INLINEFORM16 , INLINEFORM17 ) in INLINEFORM18 and then, replaces INLINEFORM19 [top] with ( INLINEFORM20 , INLINEFORM21 )  If INLINEFORM22 is valid in INLINEFORM23 , LiLi first sets the next state INLINEFORM24 and performs a sequence of operations INLINEFORM25 based on INLINEFORM26 (discussed below)  Unless specified, in INLINEFORM27 , LiLi always monitors INLINEFORM28 and if INLINEFORM29 becomes 0, LiLi sets INLINEFORM30   Also, whenever LiLi asks the user a query, INLINEFORM31 is decremented by 1  Once INLINEFORM32 ends, LiLi updates INLINEFORM33 [top] with ( INLINEFORM34 , INLINEFORM35 ) and returns INLINEFORM36 [top] to RL-model for choosing the next action 
In INLINEFORM0 , LiLi searches INLINEFORM1 , INLINEFORM2 , INLINEFORM3 in INLINEFORM4 and sets appropriate bits in INLINEFORM5 (see Table 2)  If INLINEFORM6 was unknown before and is just added to INLINEFORM7 or is in the bottom INLINEFORM8 % (see Table 1 for INLINEFORM9 ) of INLINEFORM10 , LiLi randomly sets INLINEFORM14 with probability INLINEFORM15   If INLINEFORM16 is a clue and INLINEFORM17 , LiLi updates KS with triple INLINEFORM18 , where ( INLINEFORM19 , INLINEFORM20 , INLINEFORM21 ) and ( INLINEFORM22 , INLINEFORM23 , INLINEFORM24 ) gets added to INLINEFORM25 and INLINEFORM26 , INLINEFORM27 are set as 1 
In INLINEFORM0 , LiLi asks the user to provide a clue (+ve instance) for INLINEFORM1 and corrupts INLINEFORM2 and INLINEFORM3 of the clue once at a time, to generate -ve instances by sampling nodes from INLINEFORM4   These instances help in training prediction model for INLINEFORM5 while executing INLINEFORM6  
In INLINEFORM0 , LiLi selects an incomplete path INLINEFORM1 from INLINEFORM2 to formulate MLQ, such that INLINEFORM3 is most frequently observed for INLINEFORM4 and INLINEFORM5 is high, given by INLINEFORM6   Here, INLINEFORM7 denotes the contextual similarity BIBREF16 of entity-pair INLINEFORM8   If INLINEFORM9 is high, INLINEFORM10 is more likely to possess a relation between them and so, is a good candidate for formulating MLQ  When the user does not respond to MLQ (or CLQ in INLINEFORM11 ), the guessing mechanism is used, which works as follows: Since contextual similarity of entity-pairs is highly correlated with their class labels BIBREF16 , LiLi divides the similarity range [-1, 1] into three segments, using a low ( INLINEFORM12 ) and high ( INLINEFORM13 ) similarity threshold and replaces the missing link with INLINEFORM14 in INLINEFORM15 to make it complete as follows: If INLINEFORM16 , INLINEFORM17 = “@-LooselyRelatedTo-@"; else if INLINEFORM18 , INLINEFORM19 =“@-NotRelatedTo-@"; Otherwise, INLINEFORM20 =“@-RelatedTo-@" 
In INLINEFORM0 , LiLi asks CLQs for connecting unknown entities INLINEFORM1 and/or INLINEFORM2 with INLINEFORM3 by selecting the most contextually relevant node (wrt INLINEFORM4 , INLINEFORM5 ) from INLINEFORM6 , given by link INLINEFORM7   We adopt the contextual relevance idea in BIBREF16 which is computed using word embedding BIBREF39
In INLINEFORM0 , LiLi extracts path features INLINEFORM1 between ( INLINEFORM2 , INLINEFORM3 ) and updates INLINEFORM4 with incomplete features from INLINEFORM5   LiLi always trains the prediction model with complete features INLINEFORM6 and once INLINEFORM7 or INLINEFORM8 , LiLi stops asking MLQs  Thus, in both INLINEFORM9 and INLINEFORM10 , LiLi always monitors INLINEFORM11 to check for the said requirements and sets INLINEFORM12 to control interactions 
In INLINEFORM0 , if LiLi wins the episode, it adds INLINEFORM1 in one of data buffers INLINEFORM2 based on its mode INLINEFORM3   E g , if INLINEFORM4 or INLINEFORM5 , INLINEFORM6 is used for training and added to INLINEFORM7   Similarly validation buffer INLINEFORM8 and evaluation buffer INLINEFORM9 are populated  If INLINEFORM10 , LiLi invokes the prediction model for INLINEFORM11  
Lifelong Relation Prediction  Given a relation INLINEFORM0 , LiLi uses INLINEFORM1 and INLINEFORM2 (see INLINEFORM3 ) to train a prediction model (say, INLINEFORM4 ) with parameters INLINEFORM5   For a unknown INLINEFORM6 , the clue instances get stored in INLINEFORM7 and INLINEFORM8   Thus, LiLi populates INLINEFORM9 by taking 10% (see §4) of the instances from INLINEFORM10 and starts the training  For INLINEFORM11 , LiLi uses a LSTM BIBREF40 to compose the vector representation of each feature INLINEFORM12 as INLINEFORM13 and vector representation of INLINEFORM14 as INLINEFORM15   Next, LiLi computes the prediction value, INLINEFORM16 as sigmoid of the mean cosine similarity of all features and INLINEFORM17 , given by INLINEFORM18 ) and maximize the log-likelihood of INLINEFORM19 for training  Once INLINEFORM20 is trained, LiLi updates INLINEFORM21 [ INLINEFORM22 ] using INLINEFORM23   We also train an inverse model for INLINEFORM24 , INLINEFORM25 by reversing the path features in INLINEFORM26 and INLINEFORM27 which help in lifelong learning (discussed below)  Unlike BIBREF20 , BIBREF21 , while predicting the label for INLINEFORM28 , we compute a relation-specific prediction threshold INLINEFORM29 corresponding to INLINEFORM30 using INLINEFORM31 as: INLINEFORM32 and infer INLINEFORM33 as +ve if INLINEFORM34 and -ve otherwise  Here, INLINEFORM35 ( INLINEFORM36 ) is the mean prediction value for all +ve (-ve) examples in INLINEFORM37  
Models trained on a few examples (e g , clues acquired for unknown INLINEFORM0 ) with randomly initialized weights often perform poorly due to underfitting  Thus, we transfer knowledge (weights) from the past most similar (wrt INLINEFORM1 ) task in a lifelong learning manner BIBREF31   LiLi uses INLINEFORM2 to find the past most similar task for INLINEFORM3 as follows: LiLi computes trancated SVD of INLINEFORM4 as INLINEFORM5 and then, the similarity matrix INLINEFORM6   INLINEFORM7 provides the similarity between relations INLINEFORM8 and INLINEFORM9 in INLINEFORM10   Thus, LiLi chooses a source relation INLINEFORM11 to transfer weights  Here, INLINEFORM12 is the set of all INLINEFORM13 and INLINEFORM14 for which LiLi has already learned a prediction model  Now, if INLINEFORM15 or INLINEFORM16 , LiLi randomly initializes the weights INLINEFORM17 for INLINEFORM18 and proceeds with the training  Otherwise, LiLi uses INLINEFORM19 as initial weights and fine-tunes INLINEFORM20 with a low learning rate 
A Running Example  Considering the example shown in Figure 1, LiLi works as follows: first, LiLi executes INLINEFORM0 and detects that the source entity “Obama" and query relation “CitizenOf" are unknown  Thus, LiLi executes INLINEFORM1 to acquire clue (SF1) for “CitizenOf" and pushes the clue (+ve example) and two generated -ve examples into INLINEFORM2   Once the clues are processed and a prediction model is trained for “CitizenOf" by formulating separate strategies for them, LiLi becomes aware of “CitizenOf"  Now, as the clues have already been popped from INLINEFORM3 , the query becomes INLINEFORM4 and the strategy formulation process for the query resumes  Next, LiLi asks user to provide a connecting link for “Obama" by performing INLINEFORM5   Now, the query entities and relation being known, LiLi enumerates paths between “Obama" and “USA" by performing INLINEFORM6   Let an extracted path be “ INLINEFORM7 " with missing link between ( INLINEFORM8 , INLINEFORM9 )  LiLi asks the user to fill the link by performing INLINEFORM10 and then, extracts the complete feature “ INLINEFORM11 "  The feature set is then fed to the prediction model and inference is made as a result of INLINEFORM12   Thus, the formulated inference strategy is: “ INLINEFORM13 " 
Experiments
We now evaluate LiLi in terms of its predictive performance and strategy formulation abilities 
Data: We use two standard datasets (see Table 4): (1) Freebase FB15k, and (2) WordNet INLINEFORM0   Using each dataset, we build a fairly large graph and use it as the original KB ( INLINEFORM1 ) for evaluation  We also augment INLINEFORM2 with inverse triples ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 ) for each ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) following existing KBC methods 
Parameter Settings  Unless specified, the empirically set parameters (see Table 1) of LiLi are: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 , INLINEFORM7 , INLINEFORM8 , INLINEFORM9 , INLINEFORM10   For training RL-model with INLINEFORM11 -greedy strategy, we use INLINEFORM12 , INLINEFORM13 , pre-training steps=50000  We used Keras deep learning library to implement and train the prediction model  We set batch-size as 128, max  training epoch as 150, dropout as 0 2, hidden units and embedding size as 300 and learning rate as 5e-3 which is reduced gradually on plateau with factor 0 5 and patience 5  Adam optimizer and early stopping were used in training  We also shuffle INLINEFORM14 in each epoch and adjust class weights inversely proportional to class frequencies in INLINEFORM15  
Labeled Dataset Generation and Simulated User Creation  We create a simulated user for each KB to evaluate LiLi  We create the labeled datasets, the simulated user’s knowledge base ( INLINEFORM0 ), and the base KB ( INLINEFORM1 ) from INLINEFORM2   INLINEFORM3 used as the initial KB graph ( INLINEFORM4 ) of LiLi 
We followed BIBREF16 for labeled dataset generation  For Freebase, we found 86 relations with INLINEFORM0 triples and randomly selected 50 from various domains  We randomly shuffle the list of 50 relations, select 25% of them as unknown relations and consider the rest (75%) as known relations  For each known relation INLINEFORM1 , we randomly shuffle the list of distinct triples for INLINEFORM2 , choose 1000 triples and split them into 60% training, 10% validation and 20% test  Rest 10% along with the leftover (not included in the list of 1000) triples are added to INLINEFORM3   For each unknown relation INLINEFORM4 , we remove all triples of INLINEFORM5 from INLINEFORM6 and add them to INLINEFORM7   In this process, we also randomly choose 20% triples as test instances for unknown INLINEFORM8 which are excluded from INLINEFORM9   Note that, now INLINEFORM10 has at least 10% of chosen triples for each INLINEFORM11 (known and unknown) and so, user is always able to provide clues for both cases  For each labeled dataset, we randomly choose 10% of the entities present in dataset triples, remove triples involving those entities from INLINEFORM12 and add to INLINEFORM13   At this point, INLINEFORM14 gets reduced to INLINEFORM15 and is used as INLINEFORM16 for LiLi  The dataset stats in Table 4 shows that the base KB (60% triples of INLINEFORM17 ) is highly sparse (compared to original KB) which makes the inference task much harder  WordNet dataset being small, we select all 18 relations for evaluation and create labeled dataset, INLINEFORM18 and INLINEFORM19 following Freebase  Although the user may provide clues 100% of the time, it often cannot respond to MLQs and CLQs (due to lack of required triples/facts)  Thus, we further enrich INLINEFORM20 with external KB triples 
Given a relation INLINEFORM0 and an observed triple ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 ) in training or testing, the pair ( INLINEFORM4 , INLINEFORM5 ) is regarded as a +ve instance for INLINEFORM6   Following BIBREF18 , for each +ve instance ( INLINEFORM7 , INLINEFORM8 ), we generate two negative ones, one by randomly corrupting the source INLINEFORM9 , and the other by corrupting the target INLINEFORM10   Note that, the test triples are not in INLINEFORM11 or INLINEFORM12 and none of the -ve instances overlap with the +ve ones 
Baselines  As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines 
Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations 
Sep: We do not transfer (past learned) weights for initializing INLINEFORM0 , i e , we disable LL 
F-th): Here, we use a fixed prediction threshold 0 5 instead of relation-specific threshold INLINEFORM0  
BG: The missing or connecting links (when the user does not respond) are filled with “@-RelatedTo-@" blindly, no guessing mechanism 
w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement 
Evaluation Metrics  To evaluate the strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), defined as the fraction of total query data instances, for which LiLi has successfully formulated strategies that lead to winning  If LiLi wins on all episodes for a given dataset, INLINEFORM1 is 1 0  To evaluate the predictive performance, we use Avg  MCC and avg  +ve F1 score 
Results and Analysis
Evaluation-I: Strategy Formulation Ability  Table 5 shows the list of inference strategies formulated by LiLi for various INLINEFORM0 and INLINEFORM1 , which control the strategy formulation of LiLi  When INLINEFORM2 , LiLi cannot interact with user and works like a closed-world method  Thus, INLINEFORM3 drops significantly (0 47)  When INLINEFORM4 , i e  with only one interaction per query, LiLi acquires knowledge well for instances where either of the entities or relation is unknown  However, as one unknown entity may appear in multiple test triples, once the entity becomes known, LiLi doesn’t need to ask for it again and can perform inference on future triples causing significant increase in INLINEFORM5 (0 97)  When INLINEFORM6 , LiLi is able to perform inference on all instances and INLINEFORM7 becomes 1  For INLINEFORM8 , LiLi uses INLINEFORM9 only once (as only one MLQ satisfies INLINEFORM10 ) compared to INLINEFORM11   In summary, LiLi’s RL-model can effectively formulate query-specific inference strategies (based on specified parameter values)  Evaluation-II: Predictive Performance  Table 6 shows the comparative performance of LiLi with baselines  To judge the overall improvements, we performed paired t-test considering +ve F1 scores on each relation as paired data  Considering both KBs and all relation types, LiLi outperforms Sep with INLINEFORM12   If we set INLINEFORM13 (training with very few clues), LiLi outperforms Sep with INLINEFORM14 on Freebase considering MCC  Thus, the lifelong learning mechanism is effective in transferring helpful knowledge  Single model performs better than Sep for unknown relations due to the sharing of knowledge (weights) across tasks  However, for known relations, performance drops because, as a new relation arrives to the system, old weights get corrupted and catastrophic forgetting occurs  For unknown relations, as the relations are evaluated just after training, there is no chance for catastrophic forgetting  The performance improvement ( INLINEFORM15 ) of LiLi over F-th on Freebase signifies that the relation-specific threshold INLINEFORM16 works better than fixed threshold 0 5 because, if all prediction values for test instances lie above (or below) 0 5, F-th predicts all instances as +ve (-ve) which degrades its performance  Due to the utilization of contextual similarity (highly correlated with class labels) of entity-pairs, LiLi’s guessing mechanism works better ( INLINEFORM17 ) than blind guessing (BG)  The past task selection mechanism of LiLi also improves its performance over w/o PTS, as it acquires more clues during testing for poorly performed tasks (evaluated on validation set)  For Freebase, due to a large number of past tasks [9 (25% of 38)], the performance difference is more significant ( INLINEFORM18 )  For WordNet, the number is relatively small [3 (25% of 14)] and hence, the difference is not significant 
Evaluation-III: User Interaction vs  Performance  Table 7 shows the results of LiLi by varying clue acquisition rate ( INLINEFORM0 )  We use Freebase for tuning INLINEFORM1 due to its higher number of unknown test relations compared to WordNet  LiLi’s performance improves significantly as it acquires more clues from the user  The results on INLINEFORM2 outperforms ( INLINEFORM3 ) that on INLINEFORM4   Table 8 shows the results of LiLi on user responses to MLQ’s and CLQ’s  Answering MLQ’s and CLQ’s is very hard for simulated users (unlike crowd-sourcing) as often INLINEFORM5 lacks the required triple  Thus, we attempt to analyze how the performance is effected if the user does not respond at all  The results show a clear trend in overall performance improvement when the user responds  However, the improvement is not significant as the simulated user’s query satisfaction rate (1% MLQs and 10% CLQs) is very small  But, the analysis shows the effectiveness of LiLi’s guessing mechanism and continual learning ability that help in achieving avg  +ve F1 of 0 57 and 0 62 on FB and WN respectively with minimal participation of the user 
Conclusion
In this paper, we are interested in building a generic engine for continuous knowledge learning in human-machine conversations  We first showed that the problem underlying the engine can be formulated as an open-world knowledge base completion (OKBC) problem  We then proposed an lifelong interactive learning and inference (LiLi) approach to solving the OKBC problem  OKBC is a generalization of KBC  LiLi solves the OKBC problem by first formulating a query-specific inference strategy using RL and then executing it to solve the problem by interacting with the user in a lifelong learning manner  Experimental results showed the effectiveness of LiLi in terms of both predictive quality and strategy formulation ability  We believe that a system with the LiLi approach can serve as a knowledge learning engine for conversations  Our future work will improve LiLi to make more accurate 
Acknowledgments
This work was supported in part by National Science Foundation (NSF) under grant no  IIS-1407927 and IIS-1650900, and a gift from Huawei Technologies Co Ltd 
Context 2: Introduction
Sarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule   Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech  Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities  This makes recognizing textual sarcasm more challenging for both humans and machines 
Sarcasm detection plays an indispensable role in applications like online review summarizers, dialog systems, recommendation systems and sentiment analyzers  This makes automatic detection of sarcasm an important problem  However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques  This is apparent from the results reported by the survey from DBLP:journals/corr/JoshiBC16  The following discussion brings more insights into this 
Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see because the theater has air conditioning”  It is difficult for an automatic sentiment analyzer to assign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie  This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis 
We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, using cognitive features extracted with the help of eye-tracking  A motivation to consider cognitive features comes from analyzing human eye-movement trajectories that supports the conjecture: Reading sarcastic texts induces distinctive eye movement patterns, compared to literal texts  The cognitive features, derived from human eye movement patterns observed during reading, include two primary feature types:
The cognitive features, along with textual features used in best available sarcasm detectors, are used to train binary classifiers against given sarcasm labels  Our experiments show significant improvement in classification accuracy over the state of the art, by performing such augmentation 
Related Work
Sarcasm, in general, has been the focus of research for quite some time  In one of the pioneering works jorgensen1984test explained how sarcasm arises when a figurative meaning is used opposite to the literal meaning of the utterance  In the word of clark1984pretense, sarcasm processing involves canceling the indirectly negated message and replacing it with the implicated one  giora1995irony, on the other hand, define sarcasm as a mode of indirect negation that requires processing of both negated and implicated messages  ivanko2003context define sarcasm as a six tuple entity consisting of a speaker, a listener, Context, Utterance, Literal Proposition and Intended Proposition and study the cognitive aspects of sarcasm processing 
Computational linguists have previously addressed this problem using rule based and statistical techniques, that make use of : (a) Unigrams and Pragmatic features BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 (b) Stylistic patterns BIBREF4 and patterns related to situational disparity BIBREF5 and (c) Hastag interpretations BIBREF6 , BIBREF7  
Most of the previously done work on sarcasm detection uses distant supervision based techniques (ex: leveraging hashtags) and stylistic/pragmatic features (emoticons, laughter expressions such as “lol” etc)  But, detecting sarcasm in linguistically well-formed structures, in absence of explicit cues or information (like emoticons), proves to be hard using such linguistic/stylistic features alone 
With the advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices, it has been possible to delve deep into the cognitive underpinnings of sarcasm understanding  Filik2014, using a series of eye-tracking and EEG experiments try to show that for unfamiliar ironies, the literal interpretation would be computed first  They also show that a mismatch with context would lead to a re-interpretation of the statement, as being ironic  Camblin2007103 show that in multi-sentence passages, discourse congruence has robust effects on eye movements  This also implies that disrupted processing occurs for discourse incongruent words, even though they are perfectly congruous at the sentence level  In our previous work BIBREF8 , we augment cognitive features, derived from eye-movement patterns of readers, with textual features to detect whether a human reader has realized the presence of sarcasm in text or not 
The recent advancements in the literature discussed above, motivate us to explore gaze-based cognition for sarcasm detection  As far as we know, our work is the first of its kind 
Eye-tracking Database for Sarcasm Analysis
Sarcasm often emanates from incongruity BIBREF9 , which enforces the brain to reanalyze it BIBREF10   This, in turn, affects the way eyes move through the text  Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts  This hypothesis forms the crux of our method for sarcasm detection and we validate this using our previously released freely available sarcasm dataset BIBREF8 enriched with gaze information 
Document Description
The database consists of 1,000 short texts, each having 10-40 words  Out of these, 350 are sarcastic and are collected as follows: (a) 103 sentences are from two popular sarcastic quote websites, (b) 76 sarcastic short movie reviews are manually extracted from the Amazon Movie Corpus BIBREF11 by two linguists  (c) 171 tweets are downloaded using the hashtag #sarcasm from Twitter  The 650 non-sarcastic texts are either downloaded from Twitter or extracted from the Amazon Movie Review corpus  The sentences do not contain words/phrases that are highly topic or culture specific  The tweets were normalized to make them linguistically well formed to avoid difficulty in interpreting social media lingo  Every sentence in our dataset carries positive or negative opinion about specific “aspects”  For example, the sentence “The movie is extremely well cast” has positive sentiment about the aspect “cast” 
The annotators were seven graduate students with science and engineering background, and possess good English proficiency  They were given a set of instructions beforehand and are advised to seek clarifications before they proceed  The instructions mention the nature of the task, annotation input method, and necessity of head movement minimization during the experiment 
Task Description
The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i e , positive/negative)  Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not , to rule out the Priming Effect (i e , if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 )  The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention  This is done by setting the task to polarity annotation (instead of sarcasm detection)  (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not 
The eye-tracking experiment is conducted by following the standard norms in eye-movement research BIBREF13   At a time, one sentence is displayed to the reader along with the “aspect” with respect to which the annotation has to be provided  While reading, an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz) records several eye-movement parameters like fixations (a long stay of gaze) and saccade (quick jumping of gaze between two positions of rest) and pupil size 
The accuracy of polarity annotation varies between 72%-91% for sarcastic texts and 75%-91% for non-sarcastic text, showing the inherent difficulty of sentiment annotation, when sarcasm is present in the text under consideration  Annotation errors may be attributed to: (a) lack of patience/attention while reading, (b) issues related to text comprehension, and (c) confusion/indecisiveness caused due to lack of context 
For our analysis, we do not discard the incorrect annotations present in the database  Since our system eventually aims to involve online readers for sarcasm detection, it will be hard to segregate readers who misinterpret the text  We make a rational assumption that, for a particular text, most of the readers, from a fairly large population, will be able to identify sarcasm  Under this assumption, the eye-movement parameters, averaged across all readers in our setting, may not be significantly distorted by a few readers who would have failed to identify sarcasm  This assumption is applicable for both regular and multi-instance based classifiers explained in section SECREF6  
Analysis of Eye-movement Data
We observe distinct behavior during sarcasm reading, by analyzing the “fixation duration on the text” (also referred to as “dwell time” in the literature) and “scanpaths” of the readers 
Variation in the Average Fixation Duration per Word
Since sarcasm in text can be expected to induce cognitive load, it is reasonable to believe that it would require more processing time BIBREF14   Hence, fixation duration normalized over total word count should usually be higher for a sarcastic text than for a non-sarcastic one  We observe this for all participants in our dataset, with the average fixation duration per word for sarcastic texts being at least 1 5 times more than that of non-sarcastic texts  To test the statistical significance, we conduct a two-tailed t-test (assuming unequal variance) to compare the average fixation duration per word for sarcastic and non-sarcastic texts  The hypothesized mean difference is set to 0 and the error tolerance limit ( INLINEFORM0 ) is set to 0 05  The t-test analysis, presented in Table TABREF11 , shows that for all participants, a statistically significant difference exists between the average fixation duration per word for sarcasm (higher average fixation duration) and non-sarcasm (lower average fixation duration)  This affirms that the presence of sarcasm affects the duration of fixation on words 
It is important to note that longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles  So, an elevated average fixation duration per word may not sufficiently indicate the presence of sarcasm  But we would also like to share that, for our dataset, when we considered readability (Flesch readability ease-score BIBREF15 ), number of words in a sentence and average character per word along with the sarcasm label as the predictors of average fixation duration following a linear mixed effect model BIBREF16 , sarcasm label turned out to be the most significant predictor with a maximum slope  This indicates that average fixation duration per word has a strong connection with the text being sarcastic, at least in our dataset 
We now analyze scanpaths to gain more insights into the sarcasm comprehension process 
Analysis of Scanpaths
Scanpaths are line-graphs that contain fixations as nodes and saccades as edges; the radii of the nodes represent the fixation duration  A scanpath corresponds to a participant's eye-movement pattern while reading a particular sentence
Context 3: Introduction
End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems  The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems  Several corpora have been developed in recent years  post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations  di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only  beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks  godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort  woldeyohannis provided an Amharic-English ST corpus in the tourism domain  boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7  Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs  This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9  Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings  The corpus we introduce has larger speech durations and more translation tokens  It is diversified with multiple speakers per transcript/translation  Finally, we provide additional out-of-domain test sets 
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora  We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9 3 hours of speech  Both corpora are created at the sentence level and do not require additional alignments or segmentation  Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models  CoVoST is released under CC0 license and free to use  The Tatoeba evaluation samples are also available under friendly CC licenses  All the data can be acquired at https://github com/facebookresearch/covost 
Data Collection and Processing ::: Common Voice (CoVo)
Common Voice BIBREF10 is a crowdsourcing speech recognition corpus with an open CC0 license  Contributors record voice clips by reading from a bank of donated sentences  Each voice clip was validated by at least two other users  Most of the sentences are covered by multiple speakers, with potentially different genders, age groups or accents 
Raw CoVo data contains samples that passed validation as well as those that did not  To build CoVoST, we only use the former one and reuse the official train-development-test partition of the validated data  As of January 2020, the latest CoVo 2019-06-12 release includes 29 languages  CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese 
Validated transcripts were sent to professional translators  Note that the translators had access to the transcripts but not the corresponding voice clips since clips would not carry additional information  Since transcripts were duplicated due to multiple speakers, we deduplicated the transcripts before sending them to translators  As a result, different voice clips of the same content (transcript) will have identical translations in CoVoST for train, development and test splits 
In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11  1) For German-English, French-English and Russian-English translations, we computed sentence-level BLEU BIBREF12 with the NLTK BIBREF13 implementation between the human translations and the automatic translations produced by a state-of-the-art system BIBREF14 (the French-English system was a Transformer big BIBREF15 separately trained on WMT14)  We applied this method to these three language pairs only as we are confident about the quality of the corresponding systems  Translations with a score that was too low were manually inspected and sent back to the translators when needed  2) We manually inspected examples where the source transcript was identical to the translation  3) We measured the perplexity of the translations using a language model trained on a large amount of clean monolingual data BIBREF14  We manually inspected examples where the translation had a high perplexity and sent them back to translators accordingly  4) We computed the ratio of English characters in the translations  We manually inspected examples with a low ratio and sent them back to translators accordingly  5) Finally, we used VizSeq BIBREF16 to calculate similarity scores between transcripts and translations based on LASER cross-lingual sentence embeddings BIBREF17  Samples with low scores were manually inspected and sent back for translation when needed 
We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint 
Data Collection and Processing ::: Tatoeba (TT)
Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available  Its sentences are on average shorter than those in CoVoST (see also Table TABREF2) given the original purpose of language learning  Sentences in TT are licensed under CC BY 2 0 FR and part of the speeches are available under various CC licenses 
We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVoST development and test sets  We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed  We further filter these samples by sentence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences  This makes the resulting evaluation set closer to real-world scenarios and more challenging 
We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria  Finally, we report the overlap between CoVo transcripts and TT sentences in Table TABREF5  We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST 
Data Analysis ::: Basic Statistics
Basic statistics for CoVoST and TT are listed in Table TABREF2 including (unique) sentence counts, speech durations, speaker demographics (partially available) as well as vocabulary and token statistics (based on Moses-tokenized sentences by sacreMoses) on both transcripts and translations  We see that CoVoST has over 327 hours of German speeches and over 171 hours of French speeches, which, to our knowledge, corresponds to the largest corpus among existing public ST corpora (the second largest is 110 hours BIBREF18 for German and 38 hours BIBREF19 for French)  Moreover, CoVoST has a total of 18 hours of Dutch speeches, to our knowledge, contributing the first public Dutch ST resource  CoVoST also has around 27-hour Russian speeches, 37-hour Italian speeches and 67-hour Persian speeches, which is 1 8 times, 2 5 times and 13 3 times of the previous largest public one BIBREF7  Most of the sentences (transcripts) in CoVoST are covered by multiple speakers with potentially different accents, resulting in a rich diversity in the speeches

ground_truth: Coverage, Avg. MCC and avg. +ve F1 score

strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), To evaluate the predictive performance, we use Avg. MCC and avg. +ve F1 score

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 kind of stylistic features are obtained?
Context 2:  Figure FIGREF14 presents scanpaths of three participants for the sarcastic sentence S1 and the non-sarcastic sentence S2  The x-axis of the graph represents the sequence of words a reader reads, and the y-axis represents a temporal sequence in milliseconds 
Consider a sarcastic text containing incongruous phrases A and B  Our qualitative scanpath-analysis reveals that scanpaths with respect to sarcasm processing have two typical characteristics  Often, a long regression - a saccade that goes to a previously visited segment - is observed when a reader starts reading B after skimming through A  In a few cases, the fixation duration on A and B are significantly higher than the average fixation duration per word  In sentence S1, we see long and multiple regressions from the two incongruous phrases “misconception” and “cherish”, and a few instances where phrases “always cherish” and “original misconception” are fixated longer than usual  Such eye-movement behaviors are not seen for S2 
Though sarcasm induces distinctive scanpaths like the ones depicted in Figure FIGREF14 in the observed examples, presence of such patterns is not sufficient to guarantee sarcasm; such patterns may also possibly arise from literal texts  We believe that a combination of linguistic features, readability of text and features derived from scanpaths would help discriminative machine learning models learn sarcasm better 
Features for Sarcasm Detection
We describe the features used for sarcasm detection in Table   The features enlisted under lexical,implicit incongruity and explicit incongruity are borrowed from various literature (predominantly from joshi2015harnessing)  These features are essential to separate sarcasm from other forms semantic incongruity in text (for example ambiguity arising from semantic ambiguity or from metaphors)  Two additional textual features viz  readability and word count of the text are also taken under consideration  These features are used to reduce the effect of text hardness and text length on the eye-movement patterns 
Simple Gaze Based Features
Readers' eye-movement behavior, characterized by fixations, forward saccades, skips and regressions, can be directly quantified by simple statistical aggregation (i e , either computing features for individual participants and then averaging or performing a multi-instance based learning as explained in section SECREF6 )  Since these eye-movement attributes relate to the cognitive process in reading BIBREF17 , we consider these as features in our model  Some of these features have been reported by sarcasmunderstandability for modeling sarcasm understandability of readers  However, as far as we know, these features are being introduced in NLP tasks like textual sarcasm detection for the first time  The values of these features are believed to increase with the increase in the degree of surprisal caused by incongruity in text (except skip count, which will decrease) 
Complex Gaze Based Features
For these features, we rely on a graph structure, namely “saliency graphs", derived from eye-gaze information and word sequences in the text 
For each reader and each sentence, we construct a “saliency graph”, representing the reader's attention characteristics  A saliency graph for a sentence INLINEFORM0 for a reader INLINEFORM1 , represented as INLINEFORM2 , is a graph with vertices ( INLINEFORM3 ) and edges ( INLINEFORM4 ) where each vertex INLINEFORM5 corresponds to a word in INLINEFORM6 (may not be unique) and there exists an edge INLINEFORM7 between vertices INLINEFORM8 and INLINEFORM9 if R performs at least one saccade between the words corresponding to INLINEFORM10 and INLINEFORM11  
Figure FIGREF15 shows an example of a saliency graph A saliency graph may be weighted, but not necessarily connected, for a given text (as there may be words in the given text with no fixation on them)  The “complex” gaze features derived from saliency graphs are also motivated by the theory of incongruity  For instance, Edge Density of a saliency graph increases with the number of distinct saccades, which could arise from the complexity caused by presence of sarcasm  Similarly, the highest weighted degree of a graph is expected to be higher, if the node corresponds to a phrase, incongruous to some other phrase in the text 
The Sarcasm Classifier
We interpret sarcasm detection as a binary classification problem  The training data constitutes 994 examples created using our eye-movement database for sarcasm detection  To check the effectiveness of our feature set, we observe the performance of multiple classification techniques on our dataset through a stratified 10-fold cross validation  We also compare the classification accuracy of our system and the best available systems proposed by riloff2013sarcasm and joshi2015harnessing on our dataset  Using Weka BIBREF18 and LibSVM BIBREF19 APIs, we implement the following classifiers:
Results
Table TABREF17 shows the classification results considering various feature combinations for different classifiers and other systems  These are:
Unigram (with principal components of unigram feature vectors),
Sarcasm (the feature-set reported by joshi2015harnessing subsuming unigram features and features from other reported systems)
Gaze (the simple and complex cognitive features we introduce, along with readability and word count features), and
Gaze+Sarcasm (the complete set of features) 
For all regular classifiers, the gaze features are averaged across participants and augmented with linguistic and sarcasm related features  For the MILR classifier, the gaze features derived from each participant are augmented with linguistic features and thus, a multi instance “bag” of features is formed for each sentence in the training data  This multi-instance dataset is given to an MILR classifier, which follows the standard multi instance assumption to derive class-labels for each bag 
For all the classifiers, our feature combination outperforms the baselines (considering only unigram features) as well as BIBREF3 , with the MILR classifier getting an F-score improvement of 3 7% and Kappa difference of 0 08  We also achieve an improvement of 2% over the baseline, using SVM classifier, when we employ our feature set  We also observe that the gaze features alone, also capture the differences between sarcasm and non-sarcasm classes with a high-precision but a low recall 
To see if the improvement obtained is statistically significant over the state-of-the art system with textual sarcasm features alone, we perform McNemar test  The output of the SVM classifier using only linguistic features used for sarcasm detection by joshi2015harnessing and the output of the MILR classifier with the complete set of features are compared, setting threshold INLINEFORM0   There was a significant difference in the classifier's accuracy with p(two-tailed) = 0 02 with an odds-ratio of 1 43, showing that the classification accuracy improvement is unlikely to be observed by chance in 95% confidence interval 
Considering Reading Time as a Cognitive Feature along with Sarcasm Features
One may argue that, considering simple measures of reading effort like “reading time” as cognitive feature instead of the expensive eye-tracking features for sarcasm detection may be a cost-effective solution  To examine this, we repeated our experiments with “reading time” considered as the only cognitive feature, augmented with the textual features  The F-scores of all the classifiers turn out to be close to that of the classifiers considering sarcasm feature alone and the difference in the improvement is not statistically significant ( INLINEFORM0 )  One the other hand, F-scores with gaze features are superior to the F-scores when reading time is considered as a cognitive feature 
How Effective are the Cognitive Features
We examine the effectiveness of cognitive features on the classification accuracy by varying the input training data size  To examine this, we create a stratified (keeping the class ratio constant) random train-test split of 80%:20%  We train our classifier with 100%, 90%, 80% and 70% of the training data with our whole feature set, and the feature combination from joshi2015harnessing  The goodness of our system is demonstrated by improvements in F-score and Kappa statistics, shown in Figure FIGREF22  
We further analyze the importance of features by ranking the features based on (a) Chi squared test, and (b) Information Gain test, using Weka's attribute selection module  Figure FIGREF23 shows the top 20 ranked features produced by both the tests  For both the cases, we observe 16 out of top 20 features to be gaze features  Further, in each of the cases, Average Fixation Duration per Word and Largest Regression Position are seen to be the two most significant features 
Example Cases
Table TABREF21 shows a few example cases from the experiment with stratified 80%-20% train-test split 
Example sentence 1 is sarcastic, and requires extra-linguistic knowledge (about poor living conditions at Manchester)  Hence, the sarcasm detector relying only on textual features is unable to detect the underlying incongruity  However, our system predicts the label successfully, possibly helped by the gaze features 
Similarly, for sentence 2, the false sense of presence of incongruity (due to phrases like “Helped me” and “Can't stop”) affects the system with only linguistic features  Our system, though, performs well in this case also 
Sentence 3 presents a false-negative case where it was hard for even humans to get the sarcasm  This is why our gaze features (and subsequently the complete set of features) account for erroneous prediction 
In sentence 4, gaze features alone false-indicate presence of incongruity, whereas the system predicts correctly when gaze and linguistic features are taken together 
From these examples, it can be inferred that, only gaze features would not have sufficed to rule out the possibility of detecting other forms of incongruity that do not result in sarcasm 
Error Analysis
Errors committed by our system arise from multiple factors, starting from limitations of the eye-tracker hardware to errors committed by linguistic tools and resources  Also, aggregating various eye-tracking parameters to extract the cognitive features may have caused information loss in the regular classification setting 
Conclusion
In the current work, we created a novel framework to detect sarcasm, that derives insights from human cognition, that manifests over eye movement patterns  We hypothesized that distinctive eye-movement patterns, associated with reading sarcastic text, enables improved detection of sarcasm  We augmented traditional linguistic features with cognitive features obtained from readers' eye-movement data in the form of simple gaze-based features and complex features derived from a graph structure  This extended feature-set improved the success rate of the sarcasm detector by 3 7%, over the best available system  Using cognitive features in an NLP Processing system like ours is the first proposal of its kind 
Our general approach may be useful in other NLP sub-areas like sentiment and emotion analysis, text summarization and question answering, where considering textual clues alone does not prove to be sufficient  We propose to augment this work in future by exploring deeper graph and gaze features  We also propose to develop models for the purpose of learning complex gaze feature representation, that accounts for the power of individual eye movement patterns along with the aggregated patterns of eye movements 
Acknowledgments
We thank the members of CFILT Lab, especially Jaya Jha and Meghna Singh, and the students of IIT Bombay for their help and support 
Context 3:  For example, there are over 1,000 speakers and over 10 accents in the French and German development / test sets  This enables good coverage of speech variations in both model training and evaluation 
Data Analysis ::: Speaker Diversity
As we can see from Table TABREF2, CoVoST is diversified with a rich set of speakers and accents  We further inspect the speaker demographics in terms of sample distributions with respect to speaker counts, accent counts and age groups, which is shown in Figure FIGREF6, FIGREF7 and FIGREF8  We observe that for 8 of the 11 languages, at least 60% of the sentences (transcripts) are covered by multiple speakers  Over 80% of the French sentences have at least 3 speakers  And for German sentences, even over 90% of them have at least 5 speakers  Similarly, we see that a large portion of sentences are spoken in multiple accents for French, German, Dutch and Spanish  Speakers of each language also spread widely across different age groups (below 20, 20s, 30s, 40s, 50s, 60s and 70s) 
Baseline Results
We provide baselines using the official train-development-test split on the following tasks: automatic speech recognition (ASR), machine translation (MT) and speech translation (ST) 
Baseline Results ::: Experimental Settings ::: Data Preprocessing
We convert raw MP3 audio files from CoVo and TT into mono-channel waveforms, and downsample them to 16,000 Hz  For transcripts and translations, we normalize the punctuation, we tokenize the text with sacreMoses and lowercase it  For transcripts, we further remove all punctuation markers except for apostrophes  We use character vocabularies on all the tasks, with 100% coverage of all the characters  Preliminary experimentation showed that character vocabularies provided more stable training than BPE  For MT, the vocabulary is created jointly on both transcripts and translations  We extract 80-channel log-mel filterbank features, computed with a 25ms window size and 10ms window shift using torchaudio  The features are normalized to 0 mean and 1 0 standard deviation  We remove samples having more than 3,000 frames or more than 256 characters for GPU memory efficiency (less than 25 samples are removed for all languages) 
Baseline Results ::: Experimental Settings ::: Model Training
Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing  For MT, we use a Transformer base architecture BIBREF15, but with 3 encoder layers, 3 decoder layers and 0 3 dropout  We use a batch size of 10,000 frames for ASR and ST, and a batch size of 4,000 tokens for MT  We train all models using Fairseq BIBREF20 for up to 200,000 updates  We use SpecAugment BIBREF21 for ASR and ST to alleviate overfitting 
Baseline Results ::: Experimental Settings ::: Inference and Evaluation
We use a beam size of 5 for all models  We use the best checkpoint by validation loss for MT, and average the last 5 checkpoints for ASR and ST  For MT and ST, we report case-insensitive tokenized BLEU BIBREF22 using sacreBLEU BIBREF23  For ASR, we report word error rate (WER) and character error rate (CER) using VizSeq 
Baseline Results ::: Automatic Speech Recognition (ASR)
For simplicity, we use the same model architecture for ASR and ST, although we do not leverage ASR models to pretrain ST model encoders later  Table TABREF18 shows the word error rate (WER) and character error rate (CER) for ASR models  We see that French and German perform the best given they are the two highest resource languages in CoVoST  The other languages are relatively low resource (especially Turkish and Swedish) and the ASR models are having difficulties to learn from this data 
Baseline Results ::: Machine Translation (MT)
MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation)  For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch  Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model  Table TABREF20 shows the BLEU scores of MT models  We notice that the results are consistent with what we see from ASR models  For example thanks to abundant training data, French has a decent BLEU score of 29 8/25 4  German doesn't perform well, because of less richness of content (transcripts)  The other languages are low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques 
Baseline Results ::: Speech Translation (ST)
CoVoST is a many-to-one multilingual ST corpus  While end-to-end one-to-many and many-to-many multilingual ST models have been explored very recently BIBREF8, BIBREF9, many-to-one multilingual models, to our knowledge, have not  We hence use CoVoST to examine this setting  Table TABREF22 and TABREF23 show the BLEU scores for both bilingual and multilingual end-to-end ST models trained on CoVoST  We observe that combining speeches from multiple languages is consistently bringing gains to low-resource languages (all besides French and German)  This includes combinations of distant languages, such as Ru+Fr, Tr+Fr and Zh+Fr  Moreover, some combinations do bring gains to high-resource language (French) as well: Es+Fr, Tr+Fr and Mn+Fr  We simply provide the most basic many-to-one multilingual baselines here, and leave the full exploration of the best configurations to future work  Finally, we note that for some language pairs, absolute BLEU numbers are relatively low as we restrict model training to the supervised data  We encourage the community to improve upon those baselines, for example by leveraging semi-supervised training 
Baseline Results ::: Multi-Speaker Evaluation
In CoVoST, large portion of transcripts are covered by multiple speakers with different genders, accents and age groups  Besides the standard corpus-level BLEU scores, we also want to evaluate model output variance on the same content (transcript) but different speakers  We hence propose to group samples (and their sentence BLEU scores) by transcript, and then calculate average per-group mean and average coefficient of variation defined as follows:
and
where $G$ is the set of sentence BLEU scores grouped by transcript and $G^{\prime } = \lbrace g | g\in G, |g|>1, \textrm {Mean}(g) > 0 \rbrace $ 
$\textrm {BLEU}_{MS}$ provides a normalized quality score as oppose to corpus-level BLEU or unnormalized average of sentence BLEU  And $\textrm {CoefVar}_{MS}$ is a standardized measure of model stability against different speakers (the lower the better)  Table TABREF24 shows the $\textrm {BLEU}_{MS}$ and $\textrm {CoefVar}_{MS}$ of our ST models on CoVoST test set  We see that German and Persian have the worst $\textrm {CoefVar}_{MS}$ (least stable) given their rich speaker diversity in the test set and relatively small train set (see also Figure FIGREF6 and Table TABREF2)  Dutch also has poor $\textrm {CoefVar}_{MS}$ because of the lack of training data  Multilingual models are consistantly more stable on low-resource languages  Ru+Fr, Tr+Fr, Fa+Fr and Zh+Fr even have better $\textrm {CoefVar}_{MS}$ than all individual languages 
Conclusion
We introduce a multilingual speech-to-text translation corpus, CoVoST, for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  We also provide baseline results, including, to our knowledge, the first end-to-end many-to-one multilingual model for spoken language translation  CoVoST is free to use with a CC0 license, and the additional Tatoeba evaluation samples are also CC-licensed 

ground_truth: Unanswerable

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. How big is Augmented LibriSpeech dataset?
Context 4:  This results in a Uniform Model that can select context correct responses, but not responses corresponding to a target character with specific HLAs 
We then fine-tune on the above model to produce our LSRM model with a modification: we randomly sample the 19 distractor responses from only the negative character set instead  We choose the responses that have similar grammatical structures and semantics to the ground truth response, and call this process negative character sampling  This guides the model away from the language style of these negative characters to improve performance at retrieving responses for target characters with specific HLAs  Our results demonstrate higher accuracy at retrieving the correct response from character $c_t$, which is the ground truth 
Experiment ::: Dialogue Dataset
To train the Uniform Model and LSRM, we collect dialogues from 327 major characters (a subset of the 45,821 characters we have HLA data for) in 38 TV shows from various existing sources of clean data on the internet, resulting in a total of 1,042,647 dialogue lines  We use a setup similar to the Persona-Chat dataset BIBREF0 and Cornell Movie-Dialogs Corpus BIBREF33, as our collected dialogues are also paired in terms of valid conversations  See Figure FIGREF1 for an example of these dialogue lines 
Experiment ::: HLA Observation Guidance (HLA-OG)
We define HLA Observation Guidance (HLA-OG) as explicitly passing a small subset of the most important HLAs of a given character as part of the OBS rather than just an initial line of dialogue  This is adapted from the process used in BIBREF0 zhang2018personalizing and BIBREF10 wolf2019transfertransfo which we call Persona Profiling  Specifically, we pass four HLAs that are randomly drawn from the top 40 most important HLAs of the character  We use HLA-OG during training of the LSRM and testing of all models  This is because the baselines (see Section SECREF31) already follow a similar process (Persona Profiling) for training  For the Uniform Model, we train using Next Sentence Prediction (see Section SECREF12)  For testing, HLA-OG is necessary as it provides information about which HLAs the models should attempt to imitate in their response selection  Just passing an initial line of dialogue replicates a typical dialogue response task without HLAs  See Table TABREF19  Further, we also test our LSRM by explicitly passing four HLAs of `none' along with the initial line of dialogue as the OBS (No HLA-OG in Table TABREF19) 
Experiment ::: Training Details ::: BERT bi-ranker
is trained by us on the Persona-Chat dataset for the ConvAI2 challenge  Similar to BIBREF0 zhang2018personalizing, we cap the length of the OBS at 360 tokens and the length of each candidate response at 72 tokens  We use a batch size of 64, learning rate of 5e-5, and perform warm-up updates for 100 iterations  The learning rate scheduler uses SGD optimizer with Nesterov's accelerated gradient descent BIBREF34 and is set to have a decay of 0 4 and to reduce on plateau 
Experiment ::: Training Details ::: Uniform Model
is produced by finetuning the BERT bi-ranker on the dialogue data discussed in Section SECREF15 using uniform character sampling  We use the same hyperparameters as the BERT bi-ranker along with half-precision operations (i e  float16 operations) to increase batch size as recommended BIBREF7 
Experiment ::: Training Details ::: LSRM
is produced by finetuning on the Uniform Model discussed above using negative character sampling  We use the same hyperparameters as the BERT bi-ranker along with half-precision operations (i e  float16 operations) to increase batch size as recommended 
Evaluation ::: CSM Evaluation
We begin by evaluating the ability of the CSM component of our system to correctly generate the character space  To do so, during training, 30% of the character-HLA pairs (which are either 0 or 1) are masked, and this is used as a validation set (see Figure FIGREF9)  For each character $c$, the model generates a list of the 12,815 unique HLAs ranked similarly to BIBREF29 hu2008collaborative for $c$  We look at the recall of our CSM model, which measures the percentage of total ground truth HLAs (over all characters $c$) present within the top N ranked HLAs for all $c$ by our model  That is:
where $HLA_{c}^{gt}$ are the ground truth HLAs for $c$, and $HLA_{c}^{tN}$ are the top N ranked HLAs by the model for $c$  We use $N = 100$, and our model achieves 25 08% recall 
To inspect the CSM performance, we use the T-distributed Stochastic Neighbor Embedding (t-SNE) BIBREF35 to reduce each high-dimensionality data point to two-dimensions via Kullback-Leibler Divergence BIBREF36  This allows us to map our character space into two-dimensions, where similar characters from our embedding space have higher probability of being mapped close by  We sampled characters from four different groups or regions  As seen in Figure FIGREF4, our learned character space effectively groups these characters, as similar characters are adjacent to one another in four regions 
Evaluation ::: Automatic Evaluation Setup ::: Five-Fold Cross Validation
is used for training and testing of the Uniform Model and LSRM  The folds are divided randomly by the TV shows in our dialogue data  We use the dialogue data for 80% of these shows as the four-folds for training, and the dialogue data for the remaining 20% as the fifth-fold for validation/testing  The dialogue data used is discussed in Section SECREF15  This ensures no matter how our data is distributed, each part of it is tested, allowing our evaluation to be more robust to different characters  See Appendix C for five-fold cross validation details and statistics 
Evaluation ::: Automatic Evaluation Setup ::: Five Evaluation Characters
are chosen, one from each of the five testing sets above  Each is a well-known character from a separate TV show, and acts as a target character $c_t$ for evaluation of every model  We choose Sheldon Cooper from The Big Bang Theory, Jean-Luc Picard from Star Trek, Monica Geller from Friends, Gil Grissom from CSI, and Marge Simpson from The Simpsons  We choose characters of significantly different identities and profiles (intelligent scientist, ship captain, outgoing friend, police leader, and responsible mother, respectively) from shows of a variety of genres to ensure that we can successfully recover the language styles of various types of characters  We choose well-known characters because humans require knowledge on the characters they are evaluating (see Section SECREF40) 
For each of these five evaluation characters, all the dialogue lines from the character act as the ground truth responses  The initial dialogue lines are the corresponding dialogue lines to which these ground truth responses are responding  For each initial dialogue line, we randomly sample 19 other candidate responses from the associated testing set using uniform character sampling  Note that this is for evaluation, and hence we use the same uniform character sampling method for all models including ALOHA  The use of negative character sampling is only in ALOHA's training 
Evaluation ::: Baselines
We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20  For the first three models, we use the provided pretrained (on Persona-Chat) models  We evaluate all four on our five evaluation characters discussed in Section SECREF28 
Evaluation ::: Key Evaluation Metrics ::: Hits@n/N
is the accuracy of the correct ground truth response being within the top $n$ ranked candidate responses out of $N$ total candidates  We measure Hits@1/20, Hits@5/20, and Hits@10/20 
Evaluation ::: Key Evaluation Metrics ::: Mean Rank
is the average rank that a model assigns the ground truth response among the 20 total candidates 
Evaluation ::: Key Evaluation Metrics ::: Mean Reciprocal Rank (MRR)
BIBREF37 looks at the mean of the multiplicative inverses of the rank of each correct answer out of a sample of queries $Q$:
where $rank_i$ refers to the rank position of the correct response for the $i$-th query, and $|Q|$ refers to the total number of queries in $Q$ 
Evaluation ::: Key Evaluation Metrics ::: @!START@$F_1$@!END@-score
equals $2 * \frac{precision*recall}{precision+recall}$  For dialogue, precision is the fraction of words in the chosen response contained in the ground truth response, and recall is the fraction of words in the ground truth response contained in the chosen response 
Evaluation ::: Key Evaluation Metrics ::: BLEU
BIBREF38 generally indicates how close two pieces of text are in content and structure, with higher values indicating greater similarity  We report our final BLEU scores as the average scores of 1 to 4-grams 
Evaluation ::: Human Evaluation Setup
We conduct a human evaluation with 12 participants, 8 male and 4 female, who are affiliated project researchers aged 20-39 at the University of [ANON]  We choose the same five evaluation characters as in Section SECREF28  To control bias, each participant evaluates one or two characters  For each character, we randomly select 10 testing samples (each includes an initial line of dialogue along with 20 candidate responses, one of which is the ground truth) from the same testing data for the automatic evaluation discussed in Section SECREF28 
These ten samples make up a single questionnaire presented in full to each participant evaluating the corresponding character, and the participant is asked to select the single top response they think the character would most likely respond with for each of the ten initial dialogue lines  See Figure FIGREF41 for an example  We mask any character names within the candidate responses to prevent human participants from using names to identify which show the response is from 
Each candidate is prescreened to ensure they have sufficient knowledge of the character to be a participant  We ask three prescreening questions where the participant has to identify an image, relationship, and occupation of the character  All 12 of our participants passed the the prescreening 
Results and Analysis ::: Evaluation Results
Table TABREF44 shows average results of our automatic and human evaluations  Table TABREF45 shows average Hits@1/20 scores by evaluation character  See Appendix F for detailed evaluation results  ALOHA is the model with HLA-OG during training and testing, and ALOHA (No HLA-OG) is the model with HLA-OG during training but tested with the four HLAs in the OBS marked as `none' (see Section SECREF17)  See Appendix G for demo interactions between a human, BERT bi-ranker baseline, and ALOHA for all five evaluation characters 
Results and Analysis ::: Evaluation Challenges
The evaluation of our task (retrieving the language style of a specific character) is challenging and hence the five-fold cross validation is necessary for the following reasons:
The ability to choose a context correct response without attributes of specific characters may be hard to separate from our target metric, which is the ability to retrieve the correct response of a target character by its HLAs  However, from manual observation, we noticed that in the 20 chosen candidate responses, there are typically numerous context correct responses, but only one ground truth for the target character (for an example, see Figure FIGREF41)  Hence, a model that only chooses dialogue based on context is distinguishable from one that learns HLAs 
Retrieving responses for the target character depends on the other candidate responses  For example, dialogue retrieval performance for Grissom from CSI, which is a crime/police context, is higher than other evaluation characters (see Table TABREF45), potentially due to other candidate responses not falling within the same crime/police context 
Results and Analysis ::: Performance: ALOHA vs  Humans
As observed from Table TABREF44, ALOHA has a performance relatively close to humans  Human Hits@1/20 scores have a mean of 40 67% and a median over characters of 40%  The limited human evaluation sample size limits what can be inferred, but it indicates that the problem is solved to the extent that ALOHA is able perform relatively close to humans on average  Notice that even humans do not perform extremely well, demonstrating that this task of character based dialogue retrieval is more difficult than typical dialogue retrieval tasks BIBREF19, BIBREF12 
Looking more closely at each character from Table TABREF45, we can see that human evaluation scores are higher for Sheldon and Grissom  This may be due to these characters having more distinct personalities, making them more memorable 
We also look at Pearson correlation values of the Hits@1/20 scores across the five evaluation characters  For human versus Uniform Model, this is -0 4694, demonstrating that the Uniform Model, without knowledge of HLAs, fails to imitate human impressions  For human versus ALOHA, this is 0 4250, demonstrating that our system is able to retrieve character responses somewhat similarly to human impressions  Lastly, for human versus the difference in scores between ALOHA and Uniform Model, this is 0 7815  The difference between ALOHA and the Uniform Model, which is based on the additional knowledge of the HLAs, is hence shown to improve upon the Uniform Model similarly to human impressions  This demonstrates that HLAs are indeed an accurate method of modeling human impressions of character attributes, and also demonstrates that our system, ALOHA, is able to effectively use these HLAs to improve upon dialogue retrieval performance 
Results and Analysis ::: Performance: ALOHA vs  Baselines
ALOHA, combined with the HLAs and dialogue dataset, achieves a significant improvement on the target character language style retrieval task compared to the baseline open-domain chatbot models  As observed from Table TABREF44, ALOHA achieves a significant boost in Hits@n/N accuracy and other metrics for retrieving the correct response of five diverse characters with different identities (see Section SECREF28) 
Results and Analysis ::: Performance: ALOHA vs  Uniform Model
We observe a noticeable improvement in performance between ALOHA and the Uniform Model in recovering the language styles of specific characters that is consistent across all five folds (see Tables TABREF44 and TABREF45), indicating that lack of knowledge of HLAs limits the ability of the model to successfully recover the language style of specific characters  We claim that, to the best of our knowledge, we have made the first step in using HLA-based character dialogue clustering to improve upon personality learning for chatbots 
ALOHA demonstrates an accuracy boost for all five evaluation characters, showing that the system is robust and stable and has the ability to recover the dialogue styles of fictional characters regardless of the character's profile and identity, genre of the show, and context of the dialogue 
Results and Analysis ::: Performance: HLA-OG
As observed from Table TABREF44, ALOHA performs slightly better overall compared to ALOHA (No HLA-OG)  Table TABREF45 shows that this slight performance increase is consistent across four of the five evaluation characters  In the case of Sheldon, the HLA-OG model performs a bit worse  This is possibly due to the large number of Sheldon's HLAs (217) compared to the other four evaluation characters (average of 93 75), along with the limited amount of HLAs we are using for guidance due to the models' limited memory  In general, HLA Observation Guidance during testing appears to improve upon the performance of ALOHA, but this improvement is minimal 
Conclusion and Future Work
We proposed Human Level Attributes (HLAs) as a novel approach to model human-like attributes of characters, and collected a large volume of dialogue data for various characters with complete and robust profiles  We also proposed and evaluated a system, ALOHA, that uses HLAs to recommend tailored responses traceable to specific characters, and demonstrated its outperformance of the baselines and ability to effectively recover language styles of various characters, showing promise for learning character or personality styles  ALOHA was also shown to be stable regardless of the character's identity, genre of show, and context of dialogue 
Potential directions for future work include training ALOHA with a multi-turn response approach BIBREF0 that tracks dialogue over multiple responses, as we could not acquire multi-turn dialogue data for TV shows  Another potential is the modeling of the dialog counterpart (e g  the dialogue of other characters speaking to the target character)  Further, performing semantic text exchange on the chosen response with a model such as SMERTI BIBREF39 may improve the ability of ALOHA to converse with humans  This is because the response may be context and HLA correct, but incorrect semantically (e g  the response may say the weather is sunny when it is actually rainy)  HLA-aligned generative models is another area of exploration  Typically, generative models produce text that is less fluent, but further work in this area may lead to better results  Lastly, a more diverse and larger participant pool is required due to the limited size of our human evaluation 

ground_truth: Unanswerable

Unanswerable