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Title: Semantic matching in machine reading comprehension: an empirical study
Authors: Liu, Qian
Mao, Rui
Geng, Xiubo
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Liu, Q., Mao, R., Geng, X. & Cambria, E. (2023). Semantic matching in machine reading comprehension: an empirical study. Information Processing and Management, 60(2), 103145-.
Project: A18A2b0046
Journal: Information Processing and Management
Abstract: 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 find that semantic matching is helpful for answering who/where/when/what/how/which questions, whereas it decreases the MRC performance on why questions. This may imply that semantic matching helps to answer a question whose necessary information can be retrieved from a single sentence. The above observations demonstrate the advantages and disadvantages of using semantic matching in different scenarios.
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2022.103145
Schools: School of Computer Science and Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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