Abstract
To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning. In this work, we firstly encode articles, questions and candidates to extract global reasoning information; secondly, we use multiple convolution kernels of different sizes to convolve and maximize pooling of the BERT-encoded articles, questions and candidates to extract local semantic reasoning information of different granularities; we then fuse the global information with the local multi-granularity information and use it to make an answer selection. The proposed model can combine the learned multi-granularity semantic information for reasoning, solving the problem of poor semantic reasoning ability of the model, and thus can improve the reasoning ability of machine reading comprehension. The experiments show that the proposed model achieves better performance on the C3 dataset than the benchmark model in semantic reasoning, which verifies the effectiveness of the proposed model in semantic reasoning.
Highlights
To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multigranularity semantic reasoning
Machine Reading Comprehension (MRC) tasks are similar to human reading comprehension tests in which the computer needs to answer questions based on the content of a given text [1]
Through our analysis of the multiple-choice Chinese MRC task, we found these three factors to make the task challenging: (1) few training data and lack of external knowledge severely limit the accuracy of the model; and (2) the answer selection for many questions requires deep semantic interaction to find out the corresponding answer
Summary
To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multigranularity semantic reasoning. Machine Reading Comprehension (MRC) tasks are similar to human reading comprehension tests in which the computer needs to answer questions based on the content of a given text [1]. A multiple-choice MRC task differs from a span extraction task in that it requires the text and questions and a set of candidate answers from which the machine needs to find the correct answer, taking into account the semantic information of the text [2]. In contrast to the cloze test MRC task where the answers are fixed words and phrases, the answers to the multiple-choice MRC task are artificially generated sentences that are manually rewritten with complete logic based on the content of the text. Typical English datasets of this type include MCTest [3], RACE [4] and MCScript [5], and a representative Chinese dataset is Academic Editor: Andrea Prati
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