Discriminative Reranking for Neural Machine Translation

Ann Lee, Michael Auli, Marc’Aurelio Ranzato


Abstract
Reranking models enable the integration of rich features to select a better output hypothesis within an n-best list or lattice. These models have a long history in NLP, and we revisit discriminative reranking for modern neural machine translation models by training a large transformer architecture. This takes as input both the source sentence as well as a list of hypotheses to output a ranked list. The reranker is trained to predict the observed distribution of a desired metric, e.g. BLEU, over the n-best list. Since such a discriminator contains hundreds of millions of parameters, we improve its generalization using pre-training and data augmentation techniques. Experiments on four WMT directions show that our discriminative reranking approach is effective and complementary to existing generative reranking approaches, yielding improvements of up to 4 BLEU over the beam search output.
Anthology ID:
2021.acl-long.563
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7250–7264
Language:
URL:
https://aclanthology.org/2021.acl-long.563
DOI:
10.18653/v1/2021.acl-long.563
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.563.pdf