Abstract
We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the “good” and “bad” sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.- Anthology ID:
- D18-1150
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1183–1191
- Language:
- URL:
- https://aclanthology.org/D18-1150
- DOI:
- 10.18653/v1/D18-1150
- Cite (ACL):
- Jiaji Huang, Yi Li, Wei Ping, and Liang Huang. 2018. Large Margin Neural Language Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1183–1191, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Large Margin Neural Language Model (Huang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D18-1150.pdf