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Semantic matching in machine reading comprehension: An empirical study by Qian Liu; Rui Mao; Xiubo Geng; Erik Cambria is a Computer Science article available to read on EtoBox.

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Machine reading comprehension (MRC) is a challenging task in the field of artificial intelligence. Most existing MRC works contain a semantic matching module, either explicitly or intrinsically, to determine whether a piece of context answers a question. However, there is scant work which systematically evaluates different paradigms using semantic matching in MRC. In this paper, we conduct a systematic empirical study on semantic matching. We formulate a two-stage framework which consists of a semantic matching model and a reading model, based on pre-trained language models. We compare and analyze the effectiveness and efficiency of using semantic matching modules with different setups on four types of MRC datasets. We verify that using semantic matching before a reading model improves both the effectiveness and efficiency of MRC. Compared with answering questions by extracting information from concise context, we observe that semantic matching yields more improvements for answering questions with noisy and adversarial context. Matching coarse-grained context to questions, e.g., paragraphs, is more effective than matching fine-grained context, e.g., sentences and spans. We also fin

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Author

Qian Liu; Rui Mao; Xiubo Geng; Erik Cambria

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)