@inproceedings{chung-etal-2018-supervised,
title = "Supervised and Unsupervised Transfer Learning for Question Answering",
author = "Chung, Yu-An and
Lee, Hung-Yi and
Glass, James",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1143/",
doi = "10.18653/v1/N18-1143",
pages = "1585--1594",
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."
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%0 Conference Proceedings
%T Supervised and Unsupervised Transfer Learning for Question Answering
%A Chung, Yu-An
%A Lee, Hung-Yi
%A Glass, James
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F chung-etal-2018-supervised
%X 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.
%R 10.18653/v1/N18-1143
%U https://aclanthology.org/N18-1143/
%U https://doi.org/10.18653/v1/N18-1143
%P 1585-1594
Markdown (Informal)
[Supervised and Unsupervised Transfer Learning for Question Answering](https://aclanthology.org/N18-1143/) (Chung et al., NAACL 2018)
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.