Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference
Xiaoan Ding, Tianyu Liu, Baobao Chang, Zhifang Sui, Kevin Gimpel
Abstract
While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In this paper, we focus on natural language inference (NLI). We propose GenNLI, a generative classifier for NLI tasks, and empirically characterize its performance by comparing it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work (Lewis and Fan, 2019). In particular, we find strong results with a simple unbounded modification to log loss, which we call the “infinilog loss”. Our experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise.- Anthology ID:
- 2020.emnlp-main.657
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8189–8202
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.657
- DOI:
- 10.18653/v1/2020.emnlp-main.657
- Cite (ACL):
- Xiaoan Ding, Tianyu Liu, Baobao Chang, Zhifang Sui, and Kevin Gimpel. 2020. Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8189–8202, Online. Association for Computational Linguistics.
- Cite (Informal):
- Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference (Ding et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.657.pdf
- Code
- tyliupku/gen-nli
- Data
- GLUE, MultiNLI, SICK, SNLI