Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension

Boris Galitsky, Dmitry Ilvovsky, Elizaveta Goncharova


Abstract
Machine reading comprehension (MRC) is one of the most challenging tasks in natural language processing domain. Recent state-of-the-art results for MRC have been achieved with the pre-trained language models, such as BERT and its modifications. Despite the high performance of these models, they still suffer from the inability to retrieve correct answers from the detailed and lengthy passages. In this work, we introduce a novel scheme for incorporating the discourse structure of the text into a self-attention network, and, thus, enrich the embedding obtained from the standard BERT encoder with the additional linguistic knowledge. We also investigate the influence of different types of linguistic information on the model’s ability to answer complex questions that require deep understanding of the whole text. Experiments performed on the SQuAD benchmark and more complex question answering datasets have shown that linguistic enhancing boosts the performance of the standard BERT model significantly.
Anthology ID:
2021.ranlp-1.51
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
444–453
Language:
URL:
https://aclanthology.org/2021.ranlp-1.51
DOI:
Bibkey:
Cite (ACL):
Boris Galitsky, Dmitry Ilvovsky, and Elizaveta Goncharova. 2021. Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 444–453, Held Online. INCOMA Ltd..
Cite (Informal):
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension (Galitsky et al., RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.51.pdf
Data
NewsQAQuACSQuAD