Abstract: Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.

Introduction: Machine reading comprehension and question answering has becomes a crucial application problem in evaluating the progress of AI system in the realm of natural language processing and understanding BIBREF0 . The computational linguistics communities have devoted significant attention to the general problem of machine reading comprehension and question answering. However, most of existing reading comprehension tasks only focus on shallow QA tasks that can be tackled very effectively by existing retrieval-based techniques BIBREF1 . For example, recently we have seen increased interest in constructing extractive machine reading comprehension datasets such as SQuAD BIBREF2 and NewsQA BIBREF3 . Given a document and a question, the expected answer is a short span in the document. Question context usually contains sufficient information for identifying evidence sentences that entail question-answer pairs. For example, 90.2% questions in SQuAD reported by Min BIBREF4 are answerable from the content of a single sentence. Even in some multi-turn conversation tasks, the existing models BIBREF5 mostly focus on retrieval-based response matching. In this paper, we focus on multiple-choice reading comprehension datasets such as RACE BIBREF6 in which each question comes with a set of answer options. The correct answer for most questions may not appear in the original passage which makes the task more challenging and allow a rich type of questions such as passage summarization and attitude analysis. This requires a more in-depth understanding of a single document and leverage external world knowledge to answer these questions. Besides, comparing to traditional reading comprehension problem, we need to fully consider passage-question-answer triplets instead of passage-question pairwise matching. In this paper, we propose a new model, Dual Co-Matching Network, to match a question-answer pair to a given passage bidirectionally. Our network leverages the latest breakthrough in NLP: BERT BIBREF7 contextual embedding. In the origin BERT paper, the final hidden vector corresponding to first input token ([CLS]) is used as the aggregation representation and then a standard classification loss is computed with a classification layer. We think this method is too rough to handle the passage-question-answer triplet because it only roughly concatenates the passage and question as the first sequence and uses question as the second sequence, without considering the relationship between the question and the passage. So we propose a new method to model the relationship among the passage, the question and the candidate answer. Firstly we use BERT as our encode layer to get the contextual representation of the passage, question, answer options respectively. Then a matching layer is constructed to get the passage-question-answer triplet matching representation which encodes the locational information of the question and the candidate answer matched to a specific context of the passage. Finally we apply a hierarchical aggregation method over the matching representation from word-level to sequence-level and then from sequence level to document-level. Our model improves the state-of-the-art model by 2.6 percentage on the RACE dataset with BERT base model and further improves the result by 3 percentage with BERT large model.

Question: What is their model's performance on RACE?
Answer: 67% using BERT_base, 74.1% using BERT_large, 75.8% using BERT_large, Passage, and Answer, and 82.8% using XLNET_large with Passage and Answer features