Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models

Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum


Abstract
The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However, MLE training remains the de facto approach for autoregressive NMT because of its computational efficiency and stability. Despite this mismatch between the training objective and task measure, we notice that the samples drawn from an MLE-based trained NMT support the desired distribution – there are samples with much higher BLEU score comparing to the beam decoding output. To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i.e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR). We use both marginal energy models (over target sentence) and joint energy models (over both source and target sentences). Our EBR with the joint energy model consistently improves the performance of the Transformer-based NMT: +3.7 BLEU points on IWSLT’14 German-English, +3.37 BELU points on Sinhala-English, +1.4 BLEU points on WMT’16 English-German tasks.
Anthology ID:
2021.acl-long.349
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:
4528–4537
Language:
URL:
https://aclanthology.org/2021.acl-long.349
DOI:
10.18653/v1/2021.acl-long.349
Bibkey:
Cite (ACL):
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum. 2021. Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models. In 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), pages 4528–4537, Online. Association for Computational Linguistics.
Cite (Informal):
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (Bhattacharyya et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.349.pdf
Video:
 https://aclanthology.org/2021.acl-long.349.mp4
Code
 rooshenas/ebr_mt