Supervised and Unsupervised Transfer Learning for Question Answering

Yu-An Chung, Hung-Yi Lee, James Glass

Abstract
Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.
Anthology ID:
N18-1143
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
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1585–1594
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/N18-1143/
DOI:
10.18653/v1/N18-1143
Bibkey:
Cite (ACL):
Yu-An Chung, Hung-Yi Lee, and James Glass. 2018. Supervised and Unsupervised Transfer Learning for Question Answering. 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 1585–1594, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Supervised and Unsupervised Transfer Learning for Question Answering (Chung et al., NAACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/icon-24-ingestion/N18-1143.pdf
Data
MCTest, MovieQA
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