Abstract
This paper describes our system for COIN Shared Task 1: Commonsense Inference in Everyday Narrations. To inject more external knowledge to better reason over the narrative passage, question and answer, the system adopts a stagewise fine-tuning method based on pre-trained BERT model. More specifically, the first stage is to fine-tune on addi- tional machine reading comprehension dataset to learn more commonsense knowledge. The second stage is to fine-tune on target-task (MCScript2.0) with MCScript (2018) dataset assisted. Experimental results show that our system achieves significant improvements over the baseline systems with 84.2% accuracy on the official test dataset.- Anthology ID:
- D19-6012
- Volume:
- Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Simon Ostermann, Sheng Zhang, Michael Roth, Peter Clark
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 99–103
- Language:
- URL:
- https://aclanthology.org/D19-6012
- DOI:
- 10.18653/v1/D19-6012
- Cite (ACL):
- Chunhua Liu and Dong Yu. 2019. BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations. In Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing, pages 99–103, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations (Liu & Yu, 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D19-6012.pdf