Abstract
Neural machine translation (NMT) usually employs beam search to expand the searching spaceand obtain more translation candidates. However the increase of the beam size often suffersfrom plenty of short translations resulting in dramatical decrease in translation quality. In this paper we handle the length bias problem through a perspective of causal inference. Specially we regard the model generated translation score S as a degraded true translation quality affectedby some noise and one of the confounders is the translation length. We apply a Half-Sibling Re-gression method to remove the length effect on S and then we can obtain a debiased translation score without length information. The proposed method is model agnostic and unsupervised which is adaptive to any NMT model and test dataset. We conduct the experiments on three translation tasks with different scales of datasets. Experimental results and further analyses showthat our approaches gain comparable performance with the empirical baseline methods.- Anthology ID:
- 2021.ccl-1.78
- Volume:
- Proceedings of the 20th Chinese National Conference on Computational Linguistics
- Month:
- August
- Year:
- 2021
- Address:
- Huhhot, China
- Editors:
- Sheng Li (李生), Maosong Sun (孙茂松), Yang Liu (刘洋), Hua Wu (吴华), Kang Liu (刘康), Wanxiang Che (车万翔), Shizhu He (何世柱), Gaoqi Rao (饶高琦)
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 874–885
- Language:
- English
- URL:
- https://aclanthology.org/2021.ccl-1.78
- DOI:
- Cite (ACL):
- Shi Xuewen, Huang Heyan, Jian Ping, and Tang Yi-Kun. 2021. Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 874–885, Huhhot, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method (Xuewen et al., CCL 2021)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2021.ccl-1.78.pdf