Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog

Kaixin Ma, Tomasz Jurczyk, Jinho D. Choi


Abstract
This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog. Given a dialog in text and a passage containing factual descriptions about the dialog where mentions of the characters are replaced by blanks, the task is to fill the blanks with the most appropriate character names that reflect the contexts in the dialog. Since there is no dataset that challenges the task of passage completion in this genre, we create a corpus by selecting transcripts from a TV show that comprise 1,681 dialogs, generating passages for each dialog through crowdsourcing, and annotating mentions of characters in both the dialog and the passages. Given this dataset, we build a deep neural model that integrates rich feature extraction from convolutional neural networks into sequence modeling in recurrent neural networks, optimized by utterance and dialog level attentions. Our model outperforms the previous state-of-the-art model on this task in a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs. Our analysis shows the effectiveness of the attention mechanisms and suggests a direction to machine comprehension on multiparty dialog.
Anthology ID:
N18-1185
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2039–2048
Language:
URL:
https://aclanthology.org/N18-1185
DOI:
10.18653/v1/N18-1185
Bibkey:
Cite (ACL):
Kaixin Ma, Tomasz Jurczyk, and Jinho D. Choi. 2018. Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2039–2048, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog (Ma et al., NAACL 2018)
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PDF:
https://aclanthology.org/N18-1185.pdf