Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2241–2251 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2241 Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text Jianxing Yu1,2, Zheng-Jun Zha3, Jian Yin1,2 ∗ 1School of Data and Computer Science, Sun Yat-sen University 2Guangdong Key Laboratory of Big Data Analysis and Processing, China 3School of Information Science and Technology, University of Science and Technology of China {yujx26, issjyin}@mail.sysu.edu.cn zhazj@ustc.edu.cn Abstract This paper focuses on the topic of inferential machine comprehension, which aims to ful- ly understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. In particular, we first encode the given document, question and op- tions in a context aware way. We then propose a new network to solve the inference problem by decomposing it into a series of attention- based reasoning steps. The result of the pre- vious step acts as the context of next step. To make each step can be directly inferred from the text, we design an operational cell with pri- or structure. By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by im- posing structural constraints to regulate inter- actions on the cells. Moreover, a termination mechanism is introduced to dynamically de- termine the uncertain reasoning depth, and the network is trained by reinforcement learning. Experimental results on 3 popular data sets, in- cluding MCTest, RACE and MultiRC, demon- strate the effectiveness of our approach. 1 Introduction Machine comprehension is one of the hot research topics in natural language processing. It mea- sures the machine’s ability to understand the se- mantics of a given document via answering ques- tions related to the document. Towards this task, many datasets and corresponding methods have been proposed. In most of these datasets, such as CNN/Daily Mail (Hermann et al., 2015), and SQuAD (Rajpurkar et al., 2016), the answer is often a single entity or a text span in the docu- ment. That leads to the fact that lots of ques- tions can be solved trivially via word and con- text matching (Trischler et al., 2016a) instead of ∗Corresponding author. Figure 1: Sample of question needed reasoning skill. Correct answer is marked with an asterisk genuine comprehension on text. To alleviate this issue, some datasets are released, such as M- CTest (Richardson et al., 2013), RACE (Lai et al., 2017) and MultiRC (Khashabi et al., 2018), where the answers are not restricted to be the text span- s in the document; instead, they can be described in any words. Specially, a significant proportion of questions require reasoning which is a sophis- ticated comprehension ability to choose the right answers. As shown in Figure 1, the question asks the reason for the phenomenon on sentence S5. The answer has to be deduced over the logical re- lations among sentence S3, S4 and S5, …

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