Utilizing External Knowledge with Multi-granularity Attention for Review Reading Comprehension
2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021)(2021)
Abstract
Machine Reading Comprehension (MRC) tasks are designed to enable the machine to understand a given text and answer questions. Motivated by recent success of MRC, Review Reading Comprehension (RRC) tasks using products reviews were proposed and a corresponding RRC dataset called ReviewRC was constructed. In this paper, to handle the gap between human beings and RRC models, we firstly propose an enhanced post-training approach based on BERT by data augmentation. Specifically, we utilize external knowledge including task-awareness knowledge, domain-awareness knowledge and entity-relationship knowledge to overcome task challenge and domain challenge of RRC. Also, prior knowledge is leveraged to enrich the semantic representation through sentence-level information. Then, we put forward an end-to-end model named Knowledge with Multi-granularity Attention Network (KMA-NET), in which multi-granularity attention is added to explore different levels of interactive information. Finally, experimental results on ReviewRC demonstrate that the proposed KMA-NET outperforms the baseline models.
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Key words
review reading comprehension, external knowledge, multi-granularity attention, end-to-end model
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