How State-Of-The-Art Models Can Deal With Long-Form Question Answering

Minh-Quan Bui, Vu Tran, Ha-Thanh Nguyen, Le-Minh Nguyen


Anthology ID:
2020.paclic-1.43
Volume:
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation
Month:
October
Year:
2020
Address:
Hanoi, Vietnam
Editors:
Minh Le Nguyen, Mai Chi Luong, Sanghoun Song
Venue:
PACLIC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
375–382
Language:
URL:
https://aclanthology.org/2020.paclic-1.43
DOI:
Bibkey:
Cite (ACL):
Minh-Quan Bui, Vu Tran, Ha-Thanh Nguyen, and Le-Minh Nguyen. 2020. How State-Of-The-Art Models Can Deal With Long-Form Question Answering. In Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation, pages 375–382, Hanoi, Vietnam. Association for Computational Linguistics.
Cite (Informal):
How State-Of-The-Art Models Can Deal With Long-Form Question Answering (Bui et al., PACLIC 2020)
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PDF:
https://preview.aclanthology.org/add_acl24_videos/2020.paclic-1.43.pdf
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