Abstract
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimate problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach.- Anthology ID:
- 2020.acl-main.314
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3436–3441
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.314
- DOI:
- 10.18653/v1/2020.acl-main.314
- Cite (ACL):
- Ning Miao, Yuxuan Song, Hao Zhou, and Lei Li. 2020. Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3436–3441, Online. Association for Computational Linguistics.
- Cite (Informal):
- Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (Miao et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.314.pdf
- Code
- NingMiao/MC-tailor
- Data
- DailyDialog, OntoNotes 5.0