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
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1585–1594
- Language:
- URL:
- https://aclanthology.org/N18-1143
- DOI:
- 10.18653/v1/N18-1143
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N18-1143.pdf
- Data
- MCTest, MovieQA