Machine Reading Comprehension with Enhanced Linguistic Verifiers

1 INTRODUCTION . Teaching a machine to read and comprehend large-scale textual documents is a promising and longstanding goal of natural language understanding . This field , so called machine reading comprehension ( MRC ) ( Zhang et al. , 2019 ; 2020c ) , has achieved impressive milestones in recent years thanks to the releasing of large-scale benchmark datasets and pretrained contextualized language models ( CLM ) . For example , for the well-testified span-extraction style SQuAD2.0 dataset1 , current best results under the framework of pretraining+fine-tuning employing ALBERT ( Lan et al. , 2020 ) are 90.7 % of exact matching ( EM ) and 93.0 % of F1 score , exceeds their human-level scores of 86.8 % and 89.5 % ( Rajpurkar et al. , 2016 ; 2018 ) in a large margin . MRC is traditionally defined to be a question-answering task which outputs answers by given inputs of passage-question pairs . Considering the types of answers , Chen ( 2018 ) classified MRC ’ s tasks into four categories : cloze-filling of a question with gaps ( Ghaeini et al. , 2018 ) , multiple-choice from several options ( Zhang et al. , 2020a ) , span extraction of answer from the passage ( Rajpurkar et al. , 2016 ; 2018 ; Trischler et al. , 2017 ) and free-style answer generation and summarization from the passage ( Nguyen et al. , 2016 ) . MRC is regarded to be widely applicable to numerous applications that are rich of question-style queries , such as information retrieval and task-oriented conversations . For detailed survey of this field , please refer to ( Zhang et al. , 2020c ) for recent research roadmap , datasets and future directions . In this paper , we focus on span-extraction style MRC with unanswerable questions . Rajpurkar et al . ( 2018 ) introduced 50K+ unanswerable questions to construct the SQuAD2.0 dataset . Unanswerable questions include rewriting originally answerable questions through ways of negation word inserted or removed , antonym used , entity swap , mutual exclusion , and impossible condition . Plausible answers which correspond to spans in the given passage are attached to these unanswerable questions . Numerous verifiers have been proposed to score the answerability of questions . For example , Hu et al . ( 2019 ) proposed a read-then-verify system that explicitly verified the legitimacy of the predicted answer . An answer verifier was designed to decide whether or not the predicted answer is entailable by the input snippets ( i.e. , segments of the input passage ) . Their system achieved a F1 score of 74.8 % and 74.2 % respectively on the SQuAD2.0 ’ s dev and test sets . Zhang et al . ( 2020b ) proposed a 1https : //rajpurkar.github.io/SQuAD-explorer/ pipeline with two verifiers : a sketchy reading verifier that briefly investigates the overall interactions of between a segment and a question through a binary classification network following CLM , and an intensive reading module that includes a span extractor and an answerability verifier . These two verifiers are interpolated to yield the final decision of answerablility . This framework has achieved significant improvements ( F1 score of 90.9 % and 91.4 % , respectively on the dev and test sets of SQuAD2.0 ) than the strong ALBERT baseline ( Lan et al. , 2020 ) . Minimizing span losses of start and end positions of answers for answerable questions is overwhelming in current pretraining+fine-tuning frameworks . However , there are still requirements for designing fine-grained verifiers for predicting questions ’ answerabilities by utilizing the interaction of between answer-injected questions and passages . It is valuable for us to score the linguistic correctness of the predicted answer , through replacing the interrogatives in questions by their predicted answers to check out if it is a linguistically correct sentence and in addition if the given passage can entail that rewritten question . For example , there are two reference answers ( “ P is not equal to NP ” as a complete sentence , “ not equal ” as a verb phrase ) to a question “ What implication can be derived for P and NP if P and co-NP are established to be unequal ? ” . When a system predicts “ not equal to NP. ” with unbalanced arguments ( i.e. , containing objective argument NP yet missing subjective argument P ) , it is scored 0 in exact match and discounted in F1 score . Intuitively , “ P is not equal to NP/not equal implication ... ” should be scored higher than “ not equal to NP implication ... ” . This motivates our first position-sensitive question-rewritten verifier . That is , we score the correctness and completeness of the predicted answer and the rewritten question by the existing CLM models , and build an entailment network that takes cross-attention of between the rewritten question and passage as inputs . On the other hand , the original passage/document is frequently too long to be directly used in pretraining+tuning frameworks . For example , as reported in ( Gong et al. , 2020 ) , passages in TriviaQA dataset ( Joshi et al. , 2017 ) averagely contain 2,622 tokens generated by BERT tokenizer ( Kudo & Richardson , 2018 ) in their training set . Current CLMs are incapable of accepting arbitrarily long token sequences . Thus , we are forced to cut the passage into segments with fixed length ( e.g. , 512 tokens with strides such as 128 or 256 ) . Then , the reference input to MRC now is a fix-length segment instead of the whole passage . When the manually annotated answer to the question is out of the scope of the segment , the question will be annotated to be unanswerable regardless its answerability in the whole passage . This brings bias to answerable questions since one individual segment loses its context , and it is possible that this segment implicitly contain clues for correctly answering the question . For example , in SQuAD2.0 , only one answer span is provided for one question regardless of the multiple appearances ( 104,674 appearances of answer texts for the 86,821 answerable questions ) of the same answer text in the given paragraph . For long-text MRC , Gong et al . ( 2020 ) proposed recurrent chunking mechanisms by first employing reinforcement learning to centralize the candidate answer span in the segment and then building a recurrent network to transfer contextual information among segments . Considering that it is non-trivial of data-preparation and multi-turn reasoning for training and inferencing under a reinforcement learning framework , we propose a different solution in this paper . We build a hierarchical attention network ( HAN ) ( Yang et al. , 2016 ) on sentence-level , segment-level and finally paragraph-level cross-attentions to questions for extracting and verifying candidate answers . Since the HAN framework includes segment sequences in a recurrent way , Gong et al . ( 2020 ) ’ s recurrent chunking can be regarded as a special case of ours . We combine these two types of verifiers together into a pipeline , and apply it to three span-extraction MRC benchmark sets : SQuAD2.0 ( Rajpurkar et al. , 2018 ) , NewsQA ( Trischler et al. , 2017 ) , and TriviaQA ( Joshi et al. , 2017 ) ( wikipedia part ) . Our pipeline achieves significantly better improvements on both exact matching and F1 scores than state-of-the-art baselines . 2 ENHANCED LINGUISTIC VERIFIERS . 2.1 ANSWER-INJECTED REWRITING OF QUESTIONS . The overwhelming types of questions in span-extraction MRC datasets are in a scope of 5W1H , as listed in Table 1 . NewsQA ’ s statistical information comes from their website2 . We calculate the train and validation sets together for SQuAD2.0 and TriviaQA , using these 5W1H interrogatives and their extensions ( such as “ whom ” and “ whose ” are all who-style question indicators ) . 2https : //www.microsoft.com/en-us/research/project/newsqa-dataset There are nearly 5 % questions that contain multiple interrogatives , such as WhatWDT year and whereWRB was the first perfect score given ? , WhoWP gets to choose whereWRB a gymnast starts to run on the runway ? . In order to detect interrogatives , we utilize Stanford dependency parser V4.0.0 ( Chen & Manning , 2014 ) which outputs the dependency tree together with words ’ POS tags . We inject answer text to questions with one interrogative by simply replacing it with the answer text . For questions with multiple interrogatives , we first parse the answer text and obtain its POS tag sequence and then align the interrogatives with the words in the answer text through simple heuristic rules such as whoWP for personal names , whereWRB for spaces , whenWRB for time and date . In case we fail finding the alignment , we simply attach the answer text at the left-hand-side of the question . We count on multi-head self-attention mechanism ( Vaswani et al. , 2017 ) in CLMs as a linguistic scorer to score the linguistic likelihood of the sequence . 2.2 REWRITTEN QUESTION RELATED VERIFICATION . Figure 1 illustrates our answer verification framework . We propose two types of maximum likelihood losses to score the sequences of predicted answer text and answer-injected question text by tunable pretrained CLMs . In addition , inspired by ( Zhang et al. , 2020b ) , we adapt a cross-attention span loss on the segment and rewritten question . We first integrate a question ( with |q| tokens ) and a segment ( with |p| tokens ) of a paragraph in a standard format of “ [ CLS ] question [ SEP ] segment [ SEP ] ” . Here , [ CLS ] is a classification token which is recognized to contain information of the whole sequence with a length of ( |q|+ |p|+ 3 ) . There are two separation [ SEP ] tokens respectively follow the question and the segment . The whole sequence will be sent to tunable CLMs which first embed the sequence using token , position , and segment ( tokens in “ [ CLS ] question [ SEP ] ” are assigned value of 0 , otherwise 1 ) embedding networks . Then , Transformer ’ s encoder layers are employed to represent the sequence to finally output the H tensor . Suppose that each token in H is represented by a h-dimension vector ( e.g. , h=4,096 ) . H is sent to a linear layer which projects h-dim into 2-dim , i.e. , ( batch , |q|+ |p|+ 3 , 2 ) , standing for the start/end position scores of each token in the segment . Cross entropy loss is adapted to compute the span loss , numbered with l1 in Figure 1 . Simultaneously , the vector of the first position in a sequence , i.e. , [ CLS ] ’ s representation vector is sent to a dropout layer and then a linear layer that projects h-dim into 2-dim to score the answerability of the question , by employing cross entropy loss l2 . Our proposed losses are denoted by l3 ( phrase-level ) , l4 ( sentence-level ) and l5 ( question-segment level ) in Figure 1 . We give detailed ablation experiments of these three losses in the Appendix . Note that we focus on answerable questions , i.e. , we compute these losses only when the question is answerable annotated in the train/dev sets and predicted to be answerable by the “ binary classifier ” during training/testing . One motivation is that the best-reported exact matching score for answerable questions only achieved 83.1 % while the whole exact matching score was 90.9 % , reported in ( Zhang et al. , 2020b ) . This 7.8 % gap reflects the importance of verifying the linguistic correctness of predicted answer phrases to the answerable questions . In order to compute the “ linguistic likelihood ” of these two sequences , predicted answer text and rewritten question , we first attach a [ CLS ] token to each of them and then sent them to the same CLMs . Each [ CLS ] ’ s representative vector will be further sent to a h-to-1 linear layer . The second option of computing the likelihood of the predicted answer text is to compare a similarity score ( e.g. , cosine ) of between its [ CLS ] ’ s vector and the reference answer text ’ s [ CLS ] vector .

Summary: In this paper, two linguistic verifiers are proposed to improve the model performance on machine reading comprehension datasets, such as SQuAD v2, NewsQA and TriviaQA. The first verifier rewrites the question by replacing its interrogatives with the predicted answer phrases. Then it computes a score between the rewritten question and the context, so that the answer candidates are position-sensitive. The second verifier leverages a hierarchical attention network, so that the long context can be split in to shorter segments, which are then recurrently connected to conduct answerability classification and boundary determination.