Regularization of Distinct Strategies for Unsupervised Question Generation
Junmo Kang, Giwon Hong, Haritz Puerto San Roman, Sung-Hyon Myaeng
Abstract
Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. One approach to UQA is to train a QA model with questions generated automatically. However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model. We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks. We also show that this method can be useful for creating a QA model with few-shot learning.- Anthology ID:
- 2020.findings-emnlp.293
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3266–3277
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.293
- DOI:
- 10.18653/v1/2020.findings-emnlp.293
- Cite (ACL):
- Junmo Kang, Giwon Hong, Haritz Puerto San Roman, and Sung-Hyon Myaeng. 2020. Regularization of Distinct Strategies for Unsupervised Question Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3266–3277, Online. Association for Computational Linguistics.
- Cite (Informal):
- Regularization of Distinct Strategies for Unsupervised Question Generation (Kang et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2020.findings-emnlp.293.pdf
- Code
- haritzpuerto/uqa
- Data
- HotpotQA, MRQA, Natural Questions, NewsQA, SQuAD, SearchQA, TriviaQA