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 3: Introduction
Speech-to-Text translation (ST) is essential for a wide range of scenarios: for example in emergency calls, where agents have to respond emergent requests in a foreign language BIBREF0; or in online courses, where audiences and speakers use different languages BIBREF1  To tackle this problem, existing approaches can be categorized into cascaded method BIBREF2, BIBREF3, where a machine translation (MT) model translates outputs of an automatic speech recognition (ASR) system into target language, and end-to-end method BIBREF4, BIBREF5, where a single model learns acoustic frames to target word sequence mappings in one step towards the final objective of interest  Although the cascaded model remains the dominant approach due to its better performance, the end-to-end method becomes more and more popular because it has lower latency by avoiding inferences with two models and rectifies the error propagation in theory 
Since it is hard to obtain a large-scale ST dataset, multi-task learning BIBREF5, BIBREF6 and pre-training techniques BIBREF7 have been applied to end-to-end ST model to leverage large-scale datasets of ASR and MT  A common practice is to pre-train two encoder-decoder models for ASR and MT respectively, and then initialize the ST model with the encoder of the ASR model and the decoder of the MT model  Subsequently, the ST model is optimized with the multi-task learning by weighing the losses of ASR, MT, and ST  This approach, however, causes a huge gap between pre-training and fine-tuning, which are summarized into three folds:
Subnet Waste: The ST system just reuses the ASR encoder and the MT decoder, while discards other pre-trained subnets, such as the MT encoder  Consequently, valuable semantic information captured by the MT encoder cannot be inherited by the final ST system 
Role Mismatch: The speech encoder plays different roles in pre-training and fine-tuning  The encoder is a pure acoustic model in pre-training, while it has to extract semantic and linguistic features additionally in fine-tuning, which significantly increases the learning difficulty 
Non-pre-trained Attention Module: Previous work BIBREF6 trains attention modules for ASR, MT and ST respectively, hence, the attention module of ST does not benefit from the pre-training 
To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN), which is able to reuse all subnets in pre-training, keep the roles of subnets consistent, and pre-train the attention module  Concretely, the TCEN consists of three components, a speech encoder, a text encoder, and a target text decoder  Different from the previous work that pre-trains an encoder-decoder based ASR model, we only pre-train an ASR encoder by optimizing the Connectionist Temporal Classification (CTC) BIBREF8 objective function  In this way, the additional decoder of ASR is not required while keeping the ability to read acoustic features into the source language space by the speech encoder  Besides, the text encoder and decoder can be pre-trained on a large MT dataset  After that, we employ common used multi-task learning method to jointly learn ASR, MT and ST tasks 
Compared to prior works, the encoder of TCEN is a concatenation of an ASR encoder and an MT encoder and our model does not have an ASR decoder, so the subnet waste issue is solved  Furthermore, the two encoders work at tandem, disentangling acoustic feature extraction and linguistic feature extraction, ensuring the role consistency between pre-training and fine-tuning  Moreover, we reuse the pre-trained MT attention module in ST, so we can leverage the alignment information learned in pre-training 
Since the text encoder consumes word embeddings of plausible texts in MT task but uses speech encoder outputs in ST task, another question is how one guarantees the speech encoder outputs are consistent with the word embeddings  We further modify our model to achieve semantic consistency and length consistency  Specifically, (1) the projection matrix at the CTC classification layer for ASR is shared with the word embedding matrix, ensuring that they are mapped to the same latent space, and (2) the length of the speech encoder output is proportional to the length of the input frame, so it is much longer than a natural sentence  To bridge the length gap, source sentences in MT are lengthened by adding word repetitions and blank tokens to mimic the CTC output sequences 
We conduct comprehensive experiments on the IWSLT18 speech translation benchmark BIBREF1, demonstrating the effectiveness of each component  Our model is significantly better than previous methods by 3 6 and 2 2 BLEU scores for the subword-level decoding and character-level decoding strategies, respectively 
Our contributions are three-folds: 1) we shed light on why previous ST models cannot sufficiently utilize the knowledge learned from the pre-training process; 2) we propose a new ST model, which alleviates shortcomings in existing methods; and 3) we empirically evaluate the proposed model on a large-scale public dataset 
Background ::: Problem Formulation
End-to-end speech translation aims to translate a piece of audio into a target-language translation in one step  The raw speech signals are usually converted to sequences of acoustic features, e g  Mel filterbank features  Here, we define the speech feature sequence as $\mathbf {x} = (x_1, \cdots , x_{T_x})$ The transcription and translation sequences are denoted as $\mathbf {y^{s}} = (y_1^{s}, \cdots , y_{T_s}^{s})$, and $\mathbf {y^{t}} = (y_1^{t}, \cdots , y_{T_t}^{t})$ repectively  Each symbol in $\mathbf {y^{s}}$ or $\mathbf {y^{t}}$ is an integer index of the symbol in a vocabulary $V_{src}$ or $V_{trg}$ respectively (e g  $y^s_i=k, k\in [0, |V_{src}|-1]$)  In this work, we suppose that an ASR dataset, an MT dataset, and a ST dataset are available, denoted as $\mathcal {A} = \lbrace (\mathbf {x_i}, \mathbf {y^{s}_i})\rbrace _{i=0}^I$, $\mathcal {M} =\lbrace (\mathbf {y^{s}_j}, \mathbf {y^{t}_j})\rbrace _{j=0}^J$ and $ \mathcal {S} =\lbrace (\mathbf {x_l}, \mathbf {y^{t}_l})\rbrace _{l=0}^L$ respectively  Given a new piece of audio $\mathbf {x}$, our goal is to learn an end to end model to generate a translation sentence $\mathbf {y^{t}}$ without generating an intermediate result $\mathbf {y^{s}}$ 
Background ::: Multi-Task Learning and Pre-training for ST
To leverage large scale ASR and MT data, multi-task learning and pre-training techniques are widely employed to improve the ST system  As shown in Figure FIGREF4, there are three popular multi-task strategies for ST, including 1) one-to-many setting, in which a speech encoder is shared between ASR and ST tasks; 2) many-to-one setting in which a decoder is shared between MT and ST tasks; and 3) many-to-many setting where both the encoder and decoder are shared 
A many-to-many multi-task model contains two encoders as well as two decoders  It can be jointly trained on ASR, MT, and ST tasks  As the attention module is task-specific, three attentions are defined 
Usually, the size of $\mathcal {A}$ and $\mathcal {M}$ is much larger than $\mathcal {S}$  Therefore, the common training practice is to pre-train the model on ASR and MT tasks and then fine-tune it with a multi-task learning manner  However, as aforementioned, this method suffers from subnet waste, role mismatch and non-pre-trained attention issues, which severely limits the end-to-end ST performance 
Our method
In this section, we first introduce the architecture of TCEN, which consists of two encoders connected in tandem, and one decoder with an attention module  Then we give the pre-training and fine-tuning strategy for TCEN  Finally, we propose our solutions for semantic and length inconsistency problems, which are caused by multi-task learning 
Our method ::: TCEN Architecture
Figure FIGREF5 sketches the overall architecture of TCEN, including a speech encoder $enc_s$, a text encoder $enc_t$ and a decoder $dec$ with an attention module $att$  During training, the $enc_s$ acts like an acoustic model which reads the input $\mathbf {x}$ to word or subword representations $\mathbf {h^s}$, then $enc_t$ learns high-level linguistic knowledge into hidden representations $\mathbf {h^t}$  Finally, the $dec$ defines a distribution probability over target words  The advantage of our architecture is that two encoders disentangle acoustic feature extraction and linguistic feature extraction, making sure that valuable knowledge learned from ASR and MT tasks can be effectively leveraged for ST training  Besides, every module in pre-training can be utilized in fine-tuning, alleviating the subnet waste problem 
Follow BIBREF9 inaguma2018speech, we use CNN-BiLSTM architecture to build our model  Specifically, the input features $\mathbf {x}$ are organized as a sequence of feature vectors in length $T_x$  Then, $\mathbf {x}$ is passed into a stack of two convolutional layers followed by max-pooling:
where $\mathbf {v}^{(l-1)}$ is feature maps in last layer and $\mathbf {W}^{(l)}$ is the filter  The max-pooling layers downsample the sequence in length by a total factor of four  The down-sampled feature sequence is further fed into a stack of five bidirectional $d$-dimensional LSTM layers:
where $[;]$ denotes the vector concatenation  The final output representation from the speech encoder is denoted as $\mathbf {h^s}=(h^s_1, \cdots , h^s_{\frac{T_x}{4}})$, where $h_i^s \in \mathbb {R}^d$ 
The text encoder $enc_t$ consists of two bidirectional LSTM layers  In ST task, $enc_t$ accepts speech encoder output $\mathbf {h}^s$ as input  While in MT, $enc_t$ consumes the word embedding representation $\mathbf {e^s}$ derived from $\mathbf {y^s}$, where each element $e^s_i$ is computed by choosing the $y_i^s$-th vector from the source embedding matrix $W_{E^s}$  The goal of $enc_t$ is to extract high-level linguistic features like syntactic features or semantic features from lower level subword representations $\mathbf {h}^s$ or $\mathbf {e}^s$  Since $\mathbf {h}^s$ and $\mathbf {e}^s$ belong to different latent space and have different lengths, there remain semantic and length inconsistency problems  We will provide our solutions in Section SECREF21  The output sequence of $enc_t$ is denoted as $\mathbf {h}^t$ 
The decoder is defined as two unidirectional LSTM layers with an additive attention $att$  It predicts target sequence $\mathbf {y^{t}}$ by estimating conditional probability $P(\mathbf {y^{t}}|\mathbf {x})$:
Here, $z_k$ is the the hidden state of the deocder RNN at $k$ step and $c_k$ is a time-dependent context vector computed by the attention $att$ 
Our method ::: Training Procedure
Following previous work, we split the training procedure to pre-training and fine-tuning stages  In pre-training stage, the speech encoder $enc_s$ is trained towards CTC objective using dataset $\mathcal {A}$, while the text encoder $enc_t$ and the decoder $dec$ are trained on MT dataset $\mathcal {M}$  In fine-tuning stage, we jointly train the model on ASR, MT, and ST tasks 
Our method ::: Training Procedure ::: Pre-training
To sufficiently utilize the large dataset $\mathcal {A}$ and $\mathcal {M}$, the model is pre-trained on CTC-based ASR task and MT task in the pre-training stage 
For ASR task, in order to get rid of the requirement for decoder and enable the $enc_s$ to generate subword representation, we leverage connectionist temporal classification (CTC) BIBREF8 loss to train the speech encoder 
Given an input $\mathbf {x}$, $enc_s$ emits a sequence of hidden vectors $\mathbf {h^s}$, then a softmax classification layer predicts a CTC path $\mathbf {\pi }$, where $\pi _t \in V_{src} \cup $ {`-'} is the observing label at particular RNN step $t$, and `-' is the blank token representing no observed labels:
where $W_{ctc} \in \mathbb {R}^{d \times (|V_{src}|+1)}$ is the weight matrix in the classification layer and $T$ is the total length of encoder RNN 
A legal CTC path $\mathbf {\pi }$ is a variation of the source transcription $\mathbf {y}^s$ by allowing occurrences of blank tokens and repetitions, as shown in Table TABREF14  For each transcription $\mathbf {y}$, there exist many legal CTC paths in length $T$  The CTC objective trains the model to maximize the probability of observing the golden sequence $\mathbf {y}^s$, which is calculated by summing the probabilities of all possible legal paths:
where $\Phi _T(y)$ is the set of all legal CTC paths for sequence $\mathbf {y}$ with length $T$  The loss can be easily computed using forward-backward algorithm  More details about CTC are provided in supplementary material 
For MT task, we use the cross-entropy loss as the training objective  During training, $\mathbf {y^s}$ is converted to embedding vectors $\mathbf {e^s}$ through embedding layer $W_{E^s}$, then $enc_t$ consumes $\mathbf {e^s}$ and pass the output $\mathbf {h^t}$ to decoder  The objective function is defined as:
Our method ::: Training Procedure ::: Fine-tune
In fine-tune stage, we jointly update the model on ASR, MT, and ST tasks  The training for ASR and MT follows the same process as it was in pre-training stage 
For ST task, the $enc_s$ reads the input $\mathbf {x}$ and generates $\mathbf {h^s}$, then $enc_t$ learns high-level linguistic knowledge into $\mathbf {h^t}$  Finally, the $dec$ predicts the target sentence  The ST loss function is defined as:
Following the update strategy proposed by BIBREF11 luong2015multi, we allocate a different training ratio $\alpha _i$ for each task  When switching between tasks, we select randomly a new task $i$ with probability $\frac{\alpha _i}{\sum _{j}\alpha _{j}}$ 
Our method ::: Subnet-Consistency
Our model keeps role consistency between pre-training and fine-tuning by connecting two encoders for ST task  However, this leads to some new problems: 1) The text encoder consumes $\mathbf {e^s}$ during MT training, while it accepts $\mathbf {h^s}$ during ST training  However, $\mathbf {e^s}$ and $\mathbf {h^s}$ may not follow the same distribution, resulting in the semantic inconsistency  2) Besides, the length of $\mathbf {h^s}$ is not the same order of magnitude with the length of $\mathbf {e^s}$, resulting in the length inconsistency 
In response to the above two challenges, we propose two countermeasures: 1) We share weights between CTC classification layer and source-end word embedding layer during training of ASR and MT, encouraging $\mathbf {e^s}$ and $\mathbf {h^s}$ in the same space  2)We feed the text encoder source sentences in the format of CTC path, which are generated from a seq2seq model, making it more robust toward long inputs 
Our method ::: Subnet-Consistency ::: Semantic Consistency
As shown in Figure FIGREF5, during multi-task training, two different hidden features will be fed into the text encoder $enc_t$: the embedding representation $\mathbf {e}^s$ in MT task, and the $enc_s$ output $\mathbf {h^s}$ in ST task  Without any regularization, they may belong to different latent spaces  Due to the space gap, the $enc_t$ has to compromise between two tasks, limiting its performance on individual tasks 
To bridge the space gap, our idea is to pull $\mathbf {h^s}$ into the latent space where $\mathbf {e}^s$ belong  Specifically, we share the weight $W_{ctc}$ in CTC classification layer with the source embedding weights $W_{E^s}$, which means $W_{ctc} = W_{E^s}$  In this way, when predicting the CTC path $\mathbf {\pi }$, the probability of observing the particular label $w_i \in V_{src}\cup ${`-'} at time step $t$, $p(\pi _t=w_i|\mathbf {x})$, is computed by normalizing the product of hidden vector $h_t^s$ and the $i$-th vector in $W_{E^s}$:
The loss function closes the distance between $h^s_t$ and golden embedding vector, encouraging $\mathbf {h}^s$ have the same distribution with $\mathbf {e}^s$ 
Our method ::: Subnet-Consistency ::: Length Consistency
Another existing problem is length inconsistency  The length of the sequence $\mathbf {h^s}$ is proportional to the length of the input frame $\mathbf {x}$, which is much longer than the length of $\mathbf {e^s}$  To solve this problem, we train an RNN-based seq2seq model to transform normal source sentences to noisy sentences in CTC path format, and replace standard MT with denoising MT for multi-tasking 
Specifically, we first train a CTC ASR model based on dataset $\mathcal {A} = \lbrace (\mathbf {x}_i, \mathbf {y}^s_i)\rbrace _{i=0}^{I}$, and generate a CTC-path $\mathbf {\pi }_i$ for each audio $\mathbf {x}_i$ by greedy decoding  Then we define an operation $S(\cdot )$, which converts a CTC path $\mathbf {\pi }$ to a sequence of the unique tokens $\mathbf {u}$ and a sequence of repetition times for each token $\mathbf {l}$, denoted as $S(\mathbf {\pi }) = (\mathbf {u}, \mathbf {l})$  Notably, the operation is reversible, meaning that $S^{-1} (\mathbf {u}, \mathbf {l})=\mathbf {\pi }$  We use the example $\mathbf {\pi _1}$ in Table TABREF14 and show the corresponding $\mathbf {u}$ and $\mathbf {l}$ in Table TABREF24 
Then we build a dataset $\mathcal {P} = \lbrace (\mathbf {y^s}_i, \mathbf {u}_i, \mathbf {l}_i)\rbrace _{i=0}^{I}$ by decoding all the audio pieces in $\mathcal {A}$ and transform the resulting path by the operation $S(\cdot )$  After that, we train a seq2seq model, as shown in Figure FIGREF25, which takes $ \mathbf {y^s}_i$ as input and decodes $\mathbf {u}_i, \mathbf {l}_i$ as outputs  With the seq2seq model, a noisy MT dataset $\mathcal {M}^{\prime }=\lbrace (\mathbf {\pi }_l, \mathbf {y^t}_l)\rbrace _{l=0}^{L}$ is obtained by converting every source sentence $\mathbf {y^s}_i \in \mathcal {M}$ to $\mathbf {\pi _i}$, where $\mathbf {\pi }_i = S^{-1}(\mathbf {u}_i, \mathbf {l}_i)$  We did not use the standard seq2seq model which takes $\mathbf {y^s}$ as input and generates $\mathbf {\pi }$ directly, since there are too many blank tokens `-' in $\mathbf {\pi }$ and the model tends to generate a long sequence with only blank tokens  During MT training, we randomly sample text pairs from $\mathcal {M}^{\prime }$ and $\mathcal {M}$ according to a hyper-parameter $k$  After tuning on the validation set, about $30\%$ pairs are sampled from $\mathcal {M}^{\prime }$  In this way, the $enc_t$ is more robust toward the longer inputs given by the $enc_s$ 
Experiments
We conduct experiments on the IWSLT18 speech translation task BIBREF1

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. Are the images from a specific domain?
Context 2: Introduction
Automatically generating text to describe the content of images, also known as image captioning, is a multimodal task of considerable interest in both the computer vision and the NLP communities  Image captioning can be framed as a translation task from an image to a descriptive natural language statement  Many existing captioning models BIBREF0, BIBREF1, BIBREF2, BIBREF3 follow the typical encoder-decoder framework where a convolutional network is used to condense images into visual feature representations, combined with a recurrent network for language generation  While these models demonstrate promising results, quantifying image captioning performance remains a challenging problem, in a similar way to other generative tasks BIBREF4, BIBREF5 
Evaluating candidate captions for human preference is slow and laborious  To alleviate this problem, many automatic evaluation metrics have been proposed, such as BLEU BIBREF6, METEOR BIBREF7, ROUGE BIBREF8 and CIDEr BIBREF9  These n-gram-based metrics evaluate captioning performance based on surface similarity between a candidate caption and reference statements  A more recent evaluation metric for image captioning is SPICE BIBREF10, which takes into account semantic propositional content of generated captions by scoring a caption based upon a graph-based semantic representation transformed from reference captions 
The rationale behind these evaluation metrics is that human reference captions serve as an approximate target and comparing model outputs to this target is a proxy for how well a system performs  Thus, a candidate caption is not directly evaluated with respect to image content, but compared to a set of human statements about that image 
However, in image captioning, visual scenes with multiple objects and relations correspond to a diversity of valid descriptions  Consider the example image and captions from the ShapeWorld framework BIBREF11 shown in Figure FIGREF1  The first three captions are true statements about the image and express relevant ideas, but describe different objects, attributes and spatial relationships, while the fourth caption is wrong despite referring to the same objects as in the third caption  This casts doubt on the sufficiency of using a set of reference captions to approximate the content of an image  We argue that, while existing metrics have undeniably been useful for real-world captioning evaluation, their focus on approximate surface comparison limits deeper insights into the learning process and eventual behavior of captioning models 
To address this problem, we propose a set of principled evaluation criteria which evaluate image captioning models for grammaticality, truthfulness and diversity (GTD)  These criteria correspond to necessary requirements for image captioning systems: (a) that the output is grammatical, (b) that the output statement is true with respect to the image, and (c) that outputs are diverse and mirror the variability of training captions 
Practical evaluation of GTD is currently only possible on synthetic data  We construct a range of datasets designed for image captioning evaluation  We call this diagnostic evaluation benchmark ShapeWorldICE (ShapeWorld for Image Captioning Evaluation)  We illustrate the evaluation of specific image captioning models on ShapeWorldICE  We empirically demonstrate that the existing metrics BLEU and SPICE do not capture true caption-image agreement in all scenarios, while the GTD framework allows a fine-grained investigation of how well existing models cope with varied visual situations and linguistic constructions 
We believe that as a supplementary evaluation method to real-world metrics, the GTD framework provides evaluation insights that are sufficiently interesting to motivate future work 
Related work ::: Existing evaluation of image captioning
As a natural language generation task, image captioning frequently uses evaluation metrics such as BLEU BIBREF6, METEOR BIBREF7, ROUGE BIBREF8 and CIDEr BIBREF9  These metrics use n-gram similarity between the candidate caption and reference captions to approximate the correlation between a candidate caption and the associated ground truth  SPICE BIBREF10 is a more recent metric specifically designed for image captioning  For SPICE, both the candidate caption and reference captions are parsed to scene graphs, and the agreement between tuples extracted from these scene graphs is examined  SPICE more closely relates to our truthfulness evaluation than the other metrics, but it still uses overlap comparison to reference captions as a proxy to ground truth  In contrast, our truthfulness metric directly evaluates a candidate caption against a model of the actual visual content 
Many researchers have pointed out problems with existing reference-based metrics including low correlations with human judgment BIBREF12, BIBREF10, BIBREF13 and strong baselines using nearest-neighbor methods BIBREF14 or relying solely on object detection BIBREF15  Fundamental concerns have been raised with respect to BLEU, including variability in parameterization and precise score calculation leading to significantly different results BIBREF16  Its validity as a metric for tasks other than machine translation has been questioned BIBREF17, particularly for tasks for which the output content is not narrowly constrained, like dialogue BIBREF18 
Some recent work focuses on increasing the diversity of generated captions, for which various measures are proposed  Devlin et al  BIBREF19 explored the concept of caption diversity by evaluating performance on compositionally novel images  van Miltenburg et al BIBREF20 framed image captioning as a word recall task and proposed several metrics, predominantly focusing on diversity at the word level  However, this direction is still relatively new and lacks standardized benchmarks and metrics 
Related work ::: Synthetic datasets
Recently, many synthetic datasets have been proposed as diagnostic tools for deep learning models, such as CLEVR BIBREF21 for visual question answering (VQA), the bAbI tasks BIBREF22 for text understanding and reasoning, and ShapeWorld BIBREF11 for visually grounded language understanding  The primary motivation is to reduce complexity which is considered irrelevant to the evaluation focus, to enable better control over the data, and to provide more detailed insights into strengths and limitations of existing models 
In this work, we develop the evaluation datasets within the ShapeWorld framework  ShapeWorld is a controlled data generation framework consisting of abstract colored shapes (see Figure FIGREF1 for an example)  We use ShapeWorld to generate training and evaluation data for two major reasons  ShapeWorld supports customized data generation according to user specification, which enables a variety of model inspections in terms of language construction, visual complexity and reasoning ability  Another benefit is that each training and test instance generated in ShapeWorld is returned as a triplet of $<$image, caption, world model$>$  The world model stores information about the underlying microworld used to generate an image and a descriptive caption, internally represented as a list of entities with their attributes, such as shape, color, position  During data generation, ShapeWorld randomly samples a world model from a set of available entities and attributes  The generated world model is then used to realize a corresponding instance consisting of image and caption  The world model gives the actual semantic information contained in an image, which allows evaluation of caption truthfulness 
GTD Evaluation Framework
In the following we introduce GTD in more detail, consider it as an evaluation protocol covering necessary aspects of the multifaceted captioning task, rather than a specific metric 
GTD Evaluation Framework ::: Grammaticality
An essential criterion for an image captioning model is that the captions generated are grammatically well-formed  Fully accurate assessment of grammaticality in a general context is itself a difficult task, but becomes more feasible in a very constrained context like our diagnostic language data  We take parseability with the English Resource Grammar BIBREF23 as a surrogate for grammaticality, meaning that a sentence is considered grammatically well-formed if we obtain a parse using the ERG 
The ERG is a broad-coverage grammar based on the head-driven phrase structure grammar (HPSG) framework  It is linguistically precise: sentences only parse if they are valid according to its hand-built rules  It is designed to be general-purpose: verified coverage is around 80% for Wikipedia, and over 90% for corpora with shorter sentences and more limited vocabulary (for details see BIBREF24 flickinger2011accuracy)  Since the ShapeWorld training data – the only language source for models to learn from – is generated using the same grammar, the ERG has $\sim $100% coverage of grammaticality in the model output space 
GTD Evaluation Framework ::: Truthfulness
The second aspect we investigate is truthfulness, that is, whether a candidate caption is compatible with the content of the image it is supposed to describe  We evaluate caption truthfulness on the basis of a linguistically-motivated approach using formal semantics  We convert the output of the ERG parse for a grammatical caption to a Dependency Minimal Recursion Semantics (DMRS) graph using the pydmrs tool BIBREF25  Each converted DMRS is a logical semantic graph representation corresponding to the caption  We construct a logical proposition from the DMRS graph, and evaluate it against the actual world model of the corresponding image  A caption can be said to agree with an image only if the proposition evaluates as true on the basis of the world model  By examining the logical agreement between a caption representation and a world model, we can check whether the semantics of this caption agrees with the visual content which the world model represents  Thus we do not rely on a set of captions as a surrogate for the content of an image, but instead leverage the fact that we have the ground truth, thus enabling the evaluation of true image-caption agreement 
GTD Evaluation Framework ::: Diversity
While grammaticality and truthfulness are essential requirements for image captions, these criteria alone can easily be “gamed” by specializing on a small set of generic statements which are true most of the time  In the context of abstract shapes, such captions include examples like “There is a shape” or “At least zero shapes are blue” (which is technically true even if there is no blue shape)  This motivates the third fundamental requirement of captioning output to be diverse 
As ShapeWorldICE exploits a limited size of open-class words, we emphasize the diversity in ShapeWorldICE at the sentence level rather than the word level  Since the ground-truth reference captions in ShapeWorld are randomly sampled, we take the sampled captions accompanying the test images as a proxy for optimal caption diversity, and compare it with the empirical output diversity of the evaluated model on these test images  Practically, we look at language constructions used and compute the corresponding diversity score as the ratio of observed number versus optimal number:
Language constructions here correspond to reduced caption representations which only record whether an object is described by shape (e g , “square”), color (e g , “red shape”) or color-shape combination (e g , “red square”)  So the statement “A square is red” and “A circle is blue” are considered the same, while “A shape is red” is different 
Experimental Setup ::: Datasets
We develop a variety of ShapeWorldICE datasets, with a similar idea to the “skill tasks” in the bAbI framework BIBREF22  Table TABREF4 gives an overview for different ShapeWorldICE datasets we use in this paper  We consider three different types of captioning tasks, each of which focuses on a distinct aspect of reasoning abilities  Existential descriptions examine whether a certain object is present in an image  Spatial descriptions identify spatial relationships among visual objects  Quantification descriptions involve count-based and ratio-based statements, with an explicit focus on inspecting models for their counting ability  We develop two variants for each type of dataset to enable different levels of visual complexity or specific aspects of the same reasoning type  All the training and test captions sampled in this work are in English 
Each dataset variant consists of around 200k training instances and 4,096 validation instances, plus 4,096 test instances  Each training instance consists of an image and a reference caption  At test time, only the test images are available to the evaluated models  Underlying world models are kept from the models and are used for later GTD evaluation  For each test instance, we sample ten reference captions of the same distribution as the training captions to enable the comparison of our proposed metrics to BLEU and SPICE  We fine-tune our model hyperparameters based on the performance on the validation set  All reported results are measured on the test split with the parameters yielding the best validation performance 
Experimental Setup ::: Models
We experiment with two image captioning models: the Show&Tell model BIBREF0 and the LRCN1u model BIBREF1  Both models follow the basic encoder-decoder architecture design that uses a CNN encoder to condense the visual information into an image embedding, which in turn conditions an LSTM decoder to generate a natural language caption  The main difference between the two models is the way they condition the decoder  The Show&Tell model feeds the image embedding as the “predecessor word embedding” to the first produced word, while the LRCN1u model concatenates the image features with the embedded previous word as the input to the sequence model at each time step 
We follow the common practice in image captioning to use a CNN component pretrained on object detection and fine-tune its parameters on the image captioning task  The encoder and decoder components are jointly optimized with respect to the standard cross-entropy sequence loss on the respective ShapeWorldICE dataset  For all our experiments, we train models end-to-end for a fixed number of 100k iterations with a batch size of 64  We use Adam optimization BIBREF26 with a learning rate of 0 001  Word embeddings are randomly initialized and jointly trained during the training 
Results
We train and evaluate the Show&Tell and LRCN1u models on the ShapeWorldICE datasets  Here we discuss in detail the diagnostic results of these experiments  During training, we periodically record model output on the test images, to be able to analyze the development of our evaluation metrics throughout the process  We also compute BLEU-4 scores and SPICE scores of generated captions for comparison, using 10 reference captions per test image 
LRCN1u exhibits clearly superior performance in terms of truthfulness  We start off by comparing performance of the Show&Tell model and the LRCN1u model, see Figure FIGREF8  While both models learn to produce grammatical sentences early on, it can be seen that LRCN1u is clearly superior in terms of truthfulness, achieving 100% halfway through training, whereas Show&Tell only slowly reaches around 90% by the end of 100k iterations  This indicates that incorporating visual features at every generation step is beneficial for producing true captions  The diversity ratios of captions generated by two models both increase substantially as the training progresses, with LRCN1u exhibiting a slightly greater caption diversity at the end of training 
We observed similar results on other ShapeWorldICE datasets that we experimented with, validating the superiority of LRCN1u over Show&Tell on ShapeWorldICE  Consequently, we decided to focus on the LRCN1u architecture in subsequent evaluations, where we report detailed results with respect to the GTD framework on a variety of datasets 
Correlation between the BLEU/SPICE scores and the ground truth  From the learning curves shown in Figure FIGREF9, we find low or no correlation between the BLEU/SPICE scores and caption truthfulness 
On Existential-OneShape, the BLEU curve follows the trend of the truthfulness curve in general, indicating that BLEU is able to capture caption truthfulness well in this simple scenario  However, while BLEU reports equivalent model performance on Existential-MultiShapes and Spatial-MultiShapes, the truthfulness metric demonstrates very different results  The BLEU score for generated Existential-MultiShapes captions increases rapidly at the beginning of training and then plateaus despite the continuous increase in truthfulness ratio  Captions generated on Spatial-MultiShapes attain a relatively high BLEU score from an early stage of training, but exhibit low agreement ($<$0 6 truthfulness ratio) with ground-truth visual scenes  In the case of Spatial-MultiShapes, spatial descriptors for two objects are chosen from a fixed set (“above”, “below”, “to the left of” and “to the right of”)  It is very likely for a generated spatial descriptor to match one of the descriptors mentioned in reference captions  In this particular case, the model is apt to infer a caption which has high n-gram overlaps with reference captions, resulting in a relatively high BLEU score  Thus an increased BLEU score does not necessarily indicate improved performance 
While the truthfulness and BLEU scores in Figure FIGREF9 both increase rapidly early on and then stay stable at a high rate after training for 20k iterations, the SPICE curve instead shows a downward trend in the later stage of training  We examined the output SPICE score for each test instance  SPICE reports a precision score of 1 0 for most test instances after 20k iterations, which is consistent with the truthfulness and BLEU scores  However, SPICE forms the reference scene graph as the union of the scene graphs extracted from individual reference captions, thus introducing redundancies  SPICE uses the F1 score of scene graph matching between the candidate and reference and hence is lowered by imperfect recall 
Comparing SPICE curves for three datasets shown in Figure FIGREF9-FIGREF9, they suggest an increase in task complexity, but they do not reflect the successively closing gap of caption truthfulness scores between two Existential datasets, or the substantial difference in caption truthfulness between captions on Existential-MultiShapes and Spatial-MultiShapes 
In the remainder of the paper we discuss in detail the diagnostic results of the LRCN1u model demonstrated by the GTD evaluation framework 
Perfect grammaticality for all caption types  As shown in Figure FIGREF15, generated captions for all types of ShapeWorldICE datasets attain quasi-perfect grammaticality scores in fewer than 5,000 iterations, suggesting that the model quickly learns to generate grammatically well-formed sentences 
Failure to learn complex spatial relationships  While CNNs can produce rich visual representations that can be used for a variety of vision tasks BIBREF27, it remains an open question whether these condensed visual representations are rich enough for multimodal tasks that require higher-level abilities of scene understanding and visual reasoning  From Figure FIGREF16, we can see that while the model performs rather well on Existential datasets, it exhibits a worse performance on Spatial data  The caption agreement ratio in the simple Spatial-TwoShapes scenario is relatively high, but drops significantly on Spatial-MultiShapes, demonstrating the deficiencies of the model in learning spatial relationships from complex visual scenes 
The counting task is non-trivial  Counting has long been considered to be a challenging task in multimodal reasoning BIBREF28, BIBREF29  To explore how well the LRCN1u model copes with counting tasks, we generated two Quantification datasets  The Quant-Count captions describe the number of objects with certain attributes that appear in an image (e g  “Exactly four shapes are crosses”), while the Quant-Ratio captions describe the ratio of certain objects (e g  “A third of the shapes are blue squares”) 
From Figure FIGREF16, we notice that the LRCN1u model performs poorly on these datasets in terms of truthfulness, reflected in the 0 50 and 0 46 scores achieved by the model on the Quant-Count and Quant-Ratio tasks respectively  The learning curve for Quant-Ratio exhibits a more gradual rise as the training progresses, suggesting a greater complexity for the ratio-based task 
Caption diversity benefits from varied language constructions in the training data  The diversity ratios of generated captions for different ShapeWorldICE datasets are illustrated in Figure FIGREF17  We can see that the diversity of inferred captions is largely sensitive to the caption variability in the dataset itself  For simple datasets (such as Existential-OneShape) where language constructions in the training set are less diverse, the output captions tend to have uniform sentence structures  The high diversity ratios of generated Spatial and Quantification captions suggest that caption diversity benefits from heterogeneous language constructions in complex datasets 
Discussions and Conclusions
Evaluation metrics are required as a proxy for performance in real applications  As such, they should, as far as possible, allow measurement of fundamental aspects of the performance of models on tasks  In this work, we propose the GTD evaluation framework as a supplement to standard image captioning evaluation which explicitly focuses on grammaticality, truthfulness and diversity  We developed the ShapeWorldICE evaluation suite to allow in-depth and fine-grained inspection of model behaviors  We have empirically verified that GTD captures different aspects of performance to existing metrics by evaluating image captioning models on the ShapeWorldICE suite  We hope that this framework will shed light on important aspects of model behaviour and that this will help guide future research efforts 
While performing the evaluation experiments on the LRCN1u model, we noticed that caption agreement does not always improve as the training loss decreases  Ideally, the training objective should be in accordance with how a model is eventually evaluated  In future work, we plan to investigate the feasibility of deliberately encoding the GTD signal in the training process, for instance, by implementing a GTD-aware loss  We also plan to extend the existing ShapeWorldICE benchmark to include more linguistic constructions (such as relative clauses, compound sentences and coreference)  By doing so, we hope to reveal how well existing image captioning models cope with complex generation tasks 
Acknowledgments
We thank the anonymous reviewers for their constructive feedback  HX is grateful for being supported by the CSC Cambridge Scholarship  TS is supported in part by the EPSRC Centre for Doctoral Training in Data Science, funded by the EPSRC (grant EP/L016427/1) and the University of Edinburgh  AK is grateful for being supported by a Qualcomm Research Studentship and an EPSRC Doctoral Training Studentship 
Context 3:  Since IWSLT participators use different data pre-processing methods, we reproduce several competitive baselines based on the ESPnet BIBREF12 for a fair comparison 
Experiments ::: Dataset ::: Speech translation data:
The organizer provides a speech translation corpus extracting from the TED talk (ST-TED), which consists of raw English wave files, English transcriptions, and aligned German translations  The corpus contains 272 hours of English speech with 171k segments  We split 2k segments from the corpus as dev set and tst2010, tst2013, tst2014, tst2015 are used as test sets 
Speech recognition data: Aside from ST-TED, TED-LIUM2 corpus BIBREF13 is provided as speech recognition data, which contains 207 hours of English speech and 93k transcript sentences 
Text translation data: We use transcription and translation pairs in the ST-TED corpus and WIT3 as in-domain MT data, which contains 130k and 200k sentence pairs respectively  WMT2018 is used as out-of-domain training data which consists of 41M sentence pairs 
Data preprocessing: For speech data, the utterances are segmented into multiple frames with a 25 ms window size and a 10 ms step size  Then we extract 80-channel log-Mel filter bank and 3-dimensional pitch features using Kaldi BIBREF14, resulting in 83-dimensional input features  We normalize them by the mean and the standard deviation on the whole training set  The utterances with more than 3000 frames are discarded  The transcripts in ST-TED are in true-case with punctuation while in TED-LIUM2, transcripts are in lower-case and unpunctuated  Thus, we lowercase all the sentences and remove the punctuation to keep consistent  To increase the amount of training data, we perform speed perturbation on the raw signals with speed factors 0 9 and 1 1  For the text translation data, sentences longer than 80 words or shorter than 10 words are removed  Besides, we discard pairs whose length ratio between source and target sentence is smaller than 0 5 or larger than 2 0  Word tokenization is performed using the Moses scripts and both English and German words are in lower-case 
We use two different sets of vocabulary for our experiments  For the subword experiments, both English and German vocabularies are generated using sentencepiece BIBREF15 with a fixed size of 5k tokens  BIBREF9 inaguma2018speech show that increasing the vocabulary size is not helpful for ST task  For the character experiments, both English and German sentences are represented in the character level 
For evaluation, we segment each audio with the LIUM SpkDiarization tool BIBREF16 and then perform MWER segmentation with RWTH toolkit BIBREF17  We use lowercase BLEU as evaluation metric 
Experiments ::: Baseline Models and Implementation
We compare our method with following baselines 
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder  It is trained from scratch on the ST-TED corpus 
Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained  The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus  The MT model has a text encoder and decoder with the same architecture of which in TCEN  It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data 
Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting  In the first two settings, we train the model with $\alpha _{st}=0 75$ while $\alpha _{asr}=0 25$ or $\alpha _{mt}=0 25$  For many-to-many setting, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$   For MT task, we use only in-domain data 
Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models  Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation  Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model  Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder  For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model  The Triangle model is fine-tuned under their multi-task manner 
All our baselines as well as TCEN are implemented based on ESPnet BIBREF12, the RNN size is set as $d=1024$ for all models  We use a dropout of 0 3 for embeddings and encoders, and train using Adadelta with initial learning rate of 1 0 for a maximum of 10 epochs 
For training of TCEN, we set $\alpha _{asr}=0 2$ and $\alpha _{mt}=0 8$ in the pre-training stage, since the MT dataset is much larger than ASR dataset  For fine-tune, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$, same as the `many-to-many' baseline 
For testing, we select the model with the best accuracy on speech translation task on dev set  At inference time, we use a beam size of 10, and the beam scores include length normalization with a weight of 0 2 
Experiments ::: Experimental Results
Table TABREF29 shows the results on four test sets as well as the average performance  Our method significantly outperforms the strong `many-to-many+pretrain' baseline by 3 6 and 2 2 BLEU scores respectively, indicating the proposed method is very effective that substantially improves the translation quality  Besides, both pre-training and multi-task learning can improve translation quality, and the pre-training settings (2nd-4th rows) are more effective compared to multi-task settings (5th-8th rows)  We observe a performance degradation in the `triangle+pretrain' baseline  Compared to our method, where the decoder receives higher-level syntactic and semantic linguistic knowledge extracted from text encoder, their ASR decoder can only provide lower word-level linguistic information  Besides, since their model lacks text encoder and the architecture of ST decoder is different from MT decoder, their model cannot utilize the large-scale MT data in all the training stages  Interestingly, we find that the char-level models outperform the subword-level models in all settings, especially in vanilla baseline  A similar phenomenon is observed by BIBREF6 berard2018end  A possible explanation is that learning the alignments between speech frames and subword units in another language is notoriously difficult  Our method can bring more gains in the subword setting since our model is good at learning the text-to-text alignment and the subword-level alignment is more helpful to the translation quality 
Experiments ::: Discussion ::: Ablation Study
To better understand the contribution of each component, we perform an ablation study on subword-level experiments  The results are shown in Table TABREF37  In `-MT noise' setting, we do not add noise to source sentences for MT  In `-weight sharing' setting, we use different parameters in CTC classification layer and source embedding layer  These two experiments prove that both weight sharing and using noisy MT input benefit to the final translation quality  Performance degrades more in `-weight sharing', indicating the semantic consistency contributes more to our model  In the `-pretrain' experiment, we remove the pre-training stage and directly update the model on three tasks, leading to a dramatic decrease on BLEU score, indicating the pre-training is an indispensable step for end-to-end ST 
Experiments ::: Discussion ::: Learning Curve
It is interesting to investigate why our method is superior to baselines  We find that TCEN achieves a higher final result owing to a better start-point in fine-tuning  Figure FIGREF39 provides learning curves of subword accuracy on validation set  The x-axis denotes the fine-tuning training steps  The vanilla model starts at a low accuracy, because its networks are not pre-trained on the ASR and MT data  The trends of our model and `many-to-many+pretrain' are similar, but our model outperforms it about five points in the whole fine-tuning process  It indicates that the gain comes from bridging the gap between pre-training and fine-tuning rather than a better fine-tuning process 
Experiments ::: Discussion ::: Compared with a Cascaded System
Table TABREF29 compares our model with end-to-end baselines  Here, we compare our model with cascaded systems  We build a cascaded system by combining the ASR model and MT model used in pre-training baseline  Word error rate (WER) of the ASR system and BLEU score of the MT system are reported in the supplementary material  In addition to a simple combination of the ASR and MT systems, we also re-segment the ASR outputs before feeding to the MT system, denoted as cascaded+re-seg  Specifically, we train a seq2seq model BIBREF19 on the MT dataset, where the source side is a no punctuation sentence and the target side is a natural sentence  After that, we use the seq2seq model to add sentence boundaries and punctuation on ASR outputs  Experimental results are shown in Table TABREF41  Our end-to-end model outperforms the simple cascaded model over 2 BLEU scores, and it achieves a comparable performance with the cascaded model combining with a sentence re-segment model 
Related Work
Early works conduct speech translation in a pipeline manner BIBREF2, BIBREF20, where the ASR output lattices are fed into an MT system to generate target sentences  HMM BIBREF21, DenseNet BIBREF22, TDNN BIBREF23 are commonly used ASR systems, while RNN with attention BIBREF19 and Transformer BIBREF10 are top choices for MT  To enhance the robustness of the NMT model towards ASR errors, BIBREF24 DBLP:conf/eacl/TsvetkovMD14 and BIBREF25 DBLP:conf/asru/ChenHHL17 propose to simulate the noise in training and inference 
To avoid error propagation and high latency issues, recent works propose translating the acoustic speech into text in target language without yielding the source transcription BIBREF4  Since ST data is scarce, pre-training BIBREF7, multi-task learning BIBREF4, BIBREF6, curriculum learning BIBREF26, attention-passing BIBREF27, and knowledge distillation BIBREF28, BIBREF29 strategies have been explored to utilize ASR data and MT data  Specifically, BIBREF5 DBLP:conf/interspeech/WeissCJWC17 show improvements of performance by training the ST model jointly with the ASR and the MT model  BIBREF6 berard2018end observe faster convergence and better results due to pre-training and multi-task learning on a larger dataset  BIBREF7 DBLP:conf/naacl/BansalKLLG19 show that pre-training a speech encoder on one language can improve ST quality on a different source language  All of them follow the traditional multi-task training strategies  BIBREF26 DBLP:journals/corr/abs-1802-06003 propose to use curriculum learning to improve ST performance on syntactically distant language pairs  To effectively leverage transcriptions in ST data, BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 augment the multi-task model where the target decoder receives information from the source decoder and they show improvements on low-resource speech translation  Their model just consumes ASR and ST data, in contrast, our work sufficiently utilizes the large-scale MT data to capture the rich semantic knowledge  BIBREF30 DBLP:conf/icassp/JiaJMWCCALW19 use pre-trained MT and text-to-speech (TTS) synthesis models to convert weakly supervised data into ST pairs and demonstrate that an end-to-end MT model can be trained using only synthesised data 
Conclusion
This paper has investigated the end-to-end method for ST  It has discussed why there is a huge gap between pre-training and fine-tuning in previous methods  To alleviate these issues, we have proposed a method, which is capable of reusing every sub-net and keeping the role of sub-net consistent between pre-training and fine-tuning  Empirical studies have demonstrated that our model significantly outperforms baselines 

ground_truth: Yes

Yes

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

Please answer the questions based on the text provided below. What is the attention module pretrained on?
Context 4:  If present, the word is considered as the ‘LAT’; else, the Noun in close proximity to the question word and following it is returned as the ‘LAT’ 
For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself  For the word ‘How', e g  in How many selenoproteins are encoded in the human genome?, we look at the adjective and if we find one, we take it to be the LAT, otherwise the word 'How' is considered as the ‘LAT’ 
Perhaps because of using only very simple rules, the accuracy for ‘LAT’ derivation is 75%; that is, in the remaining 25% of the cases the LAT word is identified incorrectly  Worth noting is that the overall performance the system that used LATs was slightly inferior to the system without LATs, but the types of errors changed  The training used BioBERT with the LAT feature as part of the input string  The errors it introduces usually involve finding the wrong element of the correct type e g  wrong enzyme when two similar enzymes are described in the text, or 'neuron' when asked about a type of cell with a certain function, when the answer calls for a different cell category, adipocytes, and both are mentioned in the text  We feel with more data and additional tuning or perhaps using an ensemble model, we might be able to keep the correct answers, and improve the results on the confusing examples like the one mentioned above  Therefore if we improve our ‘LAT’ derivation logic, or have larger datasets, then perhaps the neural network techniques they will yield better results 
Our Systems and Their Performance on Factoid Questions ::: Impact of Training using BioAsq data (slightly negative)
Training on BioAsq data in our entry in Batch 1 and Batch 2 under the name QA1 showed it might lead to overfitting  This happened both with (Batch 2) and without (Batch 1) hyperparameters tuning: abysmal 18% MRR in Batch 1, and slighly better one, 40% in Batch 2 (although in Batch 2 it was overall the second best result in MRR but 16% lower than the highest score) 
In Batch 3 (only), our UNCC_QA3 system was fine tuned on BioAsq and SQuAD 2 0 BIBREF7, and for data preprocessing Context paragraph is generated from relevant snippets provided in the test data  This system underperformed, by about 2% in MRR, our other entry UNCC_QA1, which was also an overall category winner for this batch  The latter was also trained on SQuAD, but not on BioAsq  We suspect that the reason could be the simplistic nature of the find() function described in Section 3 1  So, this could be an area where a better algorithm for finding the best occurrence of an entity could improve performance 
Our Systems and Their Performance on Factoid Questions ::: Impact of Using Context from URLs (negative)
In some experiments, for context in testing, we used documents for which URL pointers are provided in BioAsq  However, our system UNCC_QA3 underperformed our other system tested only on the provided snippets 
In Batch 5 the underperformance was about 6% of MRR, compared to our best system UNCC_QA1, and by 9% to the top performer 
Performance on Yes/No and List questions
Our work focused on Factoid questions  But we also have done experiments on List-type and Yes/No questions 
Performance on Yes/No and List questions ::: Entailment improves Yes/No accuracy
We started by answering always YES (in batch 2 and 3) to get the baseline performance  For batch 4 we used entailment  Our algorithm was very simple: Given a question we iterate through the candidate sentences and try to find any candidate sentence is contradicting the question (with confidence over 50%), if so 'No' is returned as answer, else 'Yes' is returned  In batch 4 this strategy produced better than the BioAsq baseline performance, and compared to our other systems, the use of entailment increased the performance by about 13% (macro F1 score)  We used 'AllenNlp' BIBREF13 entailment library to find entailment of the candidate sentences with question 
Performance on Yes/No and List questions ::: For List-type the URLs have negative impact
Overall, we followed the similar strategy that's been followed for Factoid Question Answering task  We started our experiment with batch 2, where we submitted 20 best answers (with context from snippets)  Starting with batch 3, we performed post processing: once models generate answer predictions (n-best predictions), we do post-processing on the predicted answers  In test batch 4, our system (called FACTOIDS) achieved highest recall score of ‘0 7033’ but low precision of 0 1119, leaving open the question of how could we have better balanced the two measures 
In the post-processing phase, we take the top ‘20’ (batch 3) and top 5 (batch 4 and 5), predicted answers, tokenize them using common separators: 'comma' , 'and', 'also', 'as well as'  Tokens with characters count more than ‘100’ are eliminated and rest of the tokens are added to the list of possible answers  BioASQ evaluation mechanism does not consider snippets with more than ‘100’ characters as a valid answer  Considering lengthy snippets in to the list of answers would reduce the mean precision score  As a final step, duplicate snippets in the answer pool are removed  For example, consider these top 3 answers predicted by the system (before post-processing):
{
"text": "dendritic cells",
"probability": 0 7554540733426441,
"start_logit": 8 466046333312988,
"end_logit": 9 536355018615723
},
{
"text": "neutrophils, macrophages and
distinct subtypes of dendritic cells",
"probability": 0 13806867348304214,
"start_logit": 6 766478538513184,
"end_logit": 9 536355018615723
},
{
"text": "macrophages and distinct subtypes of dendritic",
"probability": 0 013973475271178242,
"start_logit": 6 766478538513184,
"end_logit": 7 24576473236084
},
After execution of post-processing heuristics, the list of answers returned is as follows:
["dendritic cells"],
["neutrophils"],
["macrophages"],
["distinct subtypes of dendritic cells"]
Summary of our results
The tables below summarize all our results  They show that the performance of our systems was mixed  The simple architectures and algorithm we used worked very well only in Batch 3  However, we feel we can built a better system based on this experience  In particular we observed both the value of contextual embeddings and of feature engineering (LAT), however we failed to combine them properly 
Summary of our results ::: Factoid questions ::: Systems used in Batch 5 experiments
System description for ‘UNCC_QA1’: The system was finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘QA1’ : ‘LAT’ feature was added and finetuned with SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘UNCC_QA3’ : Fine tuning process is same as it is done for the system ‘UNCC_QA1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated from the relevant documents for which URLS are included in the test data 
Summary of our results ::: List Questions
For List-type questions, although post processing helped in the later batches, we never managed to obtain competitive precision, although our recall was good 
Summary of our results ::: Yes/No questions
The only thing worth remembering from our performance is that using entailment can have a measurable impact (at least with respect to a weak baseline)  The results (weak) are in Table 3 
Discussion, Future Experiments, and Conclusions ::: Summary:
In contrast to 2018, when we submitted BIBREF2 to BioASQ a system based on extractive summarization (and scored very high in the ideal answer category), this year we mainly targeted factoid question answering task and focused on experimenting with BioBERT  After these experiments we see the promise of BioBERT in QA tasks, but we also see its limitations  The latter we tried to address with mixed results using feature engineering  Overall these experiments allowed us to secure a best and a second best score in different test batches  Along with Factoid-type question, we also tried ‘Yes/No’ and ‘List’-type questions, and did reasonably well with our very simple approach 
For Yes/No the moral worth remembering is that reasoning has a potential to influence results, as evidenced by our adding the AllenNLP entailment BIBREF13 system increased its performance 
All our data and software is available at Github, in the previously referenced URL (end of Section 2) 
Discussion, Future Experiments, and Conclusions ::: Future experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding this word piece embedding directly to the BioBERT (as we did in our above experiments) would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 
We also see potential for incorporating domain specific inference into the task e g  using the MedNLI dataset BIBREF14  For all types of experiments it might be worth exploring clinical BERT embeddings BIBREF15, explicitly incorporating domain knowledge (e g  BIBREF16) and possibly deeper discourse representations (e g  BIBREF17) 
APPENDIX
In this appendix we provide additional details about the implementations 
APPENDIX ::: Systems and their descriptions:
We used several variants of our systems when experimenting with the BioASQ problems  In retrospect, it would be much easier to understand the changes if we adopted some mnemonic conventions in naming the systems  So, we apologize for the names that do not reflect the modifications, and necessitate this list 
APPENDIX ::: Systems and their descriptions: ::: Factoid Type Question Answering:
We preprocessed the test data to convert test data to BioBERT format, We generated Context/paragraph by either aggregating relevant snippets provided or by aggregating documents for which URLS are provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: System description for QA1:
We generated Context/paragraph by aggregating relevant snippets available in the test data and mapped it against the question text and question id  We ignored the content present in the documents (document URLS were provided in the original test data)  The model is finetuned with BioASQ data 
data preprocessing is done in the same way as it is done for test batch-1  Model fine tuned on BioASQ data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA_1:
System is finetuned on the SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA3:
System is finetuned on the SQuAD 2 0 [reference] and BioASQ dataset[] For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
Fine tuning process is same as it is done for the system ‘UNCC_QA_1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated form from the relevant documents for which URLS are included in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA2:
Fine tuning process is same as for ‘UNCC_QA_1 ’  Difference is Context/paragraph is generated form from the relevant documents for which URLS are included in the test data  System ‘UNCC_QA_1’ got the highest ‘MRR’ score in the 3rd test batch set 
APPENDIX ::: Systems and their descriptions: ::: System description for FACTOIDS:
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: List Type Questions:
We attempted List type questions starting from test batch ‘2’  Used similar approach that's been followed for Factoid Question answering task  For all the test batch sets, in the data pre processing phase Context/ paragraph is generated either by aggregating relevant snippets or by aggregating documents(URLS) provided in the BioASQ test data 
For test batch-2, model (System: QA1) is finetuned on BioASQ data and submitted top ‘20’ answers predicted by the model as the list of answers  system ‘QA1’ achieved low F-Measure score:‘0 0786’ in the second test batch  In the further test batches for List type questions, we finetuned the model on Squad data set [reference], implemented post processing techniques (refer section 5 2) and achieved a better F-measure score: ‘0 2862’ in the final test batch set 
In test batch-3 (Systems : ‘QA1’/’’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’) top 20 answers returned by the model is sent for post processing and in test batch 4 and 5 only top 5 answers are sent for post processing  System UNCC_QA2(in batch 3) for List type question answering, Context is generated from documents for which URLS are provided in the BioASQ test data  for the rest of the systems (in test batch-3) for List Type question answering task snippets present in the BioaSQ test data are used to generate context 
In test batch-4 (System : ‘FACTOIDS’/’UNCC_QA_1’/’UNCC_QA3’’) top 5 answers returned by the model is sent for post processing  In case of system ‘FACTOIDS’ snippets in the test data were used to generate context  for systems ’UNCC_QA_1’ and ’UNCC_QA3’ context is generated from the documents for which URLS are provided in the BioASQ test data 
In test batch-5 ( Systems: ‘QA1’/’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’ ) our approach is the same as that of test batch-4 where top 5 answers returned by the model is sent for post processing  for all the systems (in test batch-5) context is generated from the snippets provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: Yes/No Type Questions:
For the first 3 test batches, We have submitted answer ‘Yes’ to all the questions  Later, we employed ‘Sentence Entailment’ techniques(refer section 6 0) for the fourth and fifth test batch sets  Our Systems with ‘Sentence Entailment’ approach (for ‘Yes’/ ‘No’ question answering): ‘UNCC_QA_1’(test batch-4), UNCC_QA3(test batch-5) 
APPENDIX ::: Additional details for Yes/No Type Questions
We used Textual Entailment in Batch 4 and 5 for ‘Yes’/‘No’ question type  The algorithm was very simple: Given a question we iterate through the candidate sentences, and look for any candidate sentences contradicting the question  If we find one 'No' is returned as answer, else 'Yes' is returned  (The confidence for contradiction was set at 50%) We used AllenNLP BIBREF13 entailment library to find entailment of the candidate sentences with question 
Flow Chart for Yes/No Question answer processing is shown in Fig FIGREF51
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions
There are different question types, and we distinguished them based on the question words: ‘Which’, ‘What’, ‘When’, ‘How’ etc  Each type of question is being handled differently and there are commonalities among the rules written for different question types  How are question words identified? question words have parts of speech(POS): 'WDT', 'WRB', 'WP' 
Assumptions:
1) Lexical answer type (‘LAT’) or focus word is of type Noun and follows the question word 
2) The LAT word is a Subject  (This clearly not always true, but we used a very simple method)  Note: ‘StanfordNLP’ dependency parsing tag for identifying subject is 'nsubj' or 'nsubjpass' 
3) When a question has multiple words that are of type Subject (and Noun), a word that is in proximity to the question word is considered as ‘LAT’ 
4) For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself that is, ‘When’, ‘Who’, ‘Why’ respectively 
Rules and logic flow to traverse a question: The three cases below describe the logic flow of finding LATs  The figures show the grammatical structures used for this purpose 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-1:
Question with question word ‘How’ 
For questions with question word 'How', the adjective that follows the question word is considered as ‘LAT’ (need not follow immediately)  If an adjective is absent, word 'How' is considered as ‘LAT’  When there are multiple words that are adjectives, a word in close proximity to the question word and follows it is returned as ‘LAT’  Note: The part of speech tag to identify adjectives is 'JJ'  For Other possible question words like ‘whose’  ‘LAT’/Focus word is question words itself 
Example Question: How many selenoproteins are encoded in the human genome?
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-2:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; a 'Noun' immediately following the question word 
Example Question: Which enzyme is targeted by Evolocumab?
Here, Focus word/LAT is ‘enzyme’ which is both Noun and Subject and immediately follows the question word 
When the word immediately following the question word is a noun, the window size is set to ‘3’  This size ‘3’ means that we iterate through the next ‘3’ words (if present) to check if any of the word is both 'Noun' and 'Subject', If so, the word is considered as ‘LAT’/Focus Word  Else the word that is present very next to the question word is considered as ‘LAT’ 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-3:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; word immediately following the question word is not a 'Noun' 
Example Question: What is the function of the protein Magt1?
Here, Focus word/LAT is ‘function ’ which is both Noun and Subject and does not immediately follow the question word 
When the very next word following the question word is not a Noun, window size is set to ‘5’  Window size ‘5’ corresponds that we iterate through the next ‘5’ words (if present) and search for the word that is both Noun and Subject  If present, the word is considered as ‘LAT’  Else, the 'Noun' close proximity to the question word and follows it is returned as ‘LAT’ 
Ad we mentioned earlier, the accuracy for ‘LAT’ derivation is 75 percent  But clearly the simple logic described above can be improved, as shown in BIBREF9, BIBREF10  Whether this in turn produces improvements in this particular task is an open question 
APPENDIX ::: Proposing Future Experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering Neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In another experiment we would like to only feed contextual word embeddings for Focus word/ ‘LAT’, paragraph/ Context as input to the question answering neural network  In this experiment we would neglect all embeddings for the question text except that of Focus word/ ‘LAT’  Our assumption and idea for considering focus word and neglecting remaining words in the question is that during training phase it would make more precise for the model to identify the focus of the question and map answers against the question’s focus  To validate our assumption, we would like to take sample question answering data and find the cosine distance between contextual embedding of Focus word and that of the actual answer and verify if the cosine distance is comparatively low in most of the cases 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding Focus word’s word piece embedding directly (as we did in our above experiments) to the BioBERT would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 

ground_truth: the model is pre-trained on CTC-based ASR task and MT task in the pre-training stage.