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    <abstract>There exists a discrepancy between the token-level objective during training and the overall sequence-level quality that is expected from the model. This discrepancy leads to issues like exposure bias.To align the model with human expectations, sequence-level objectives are often used to fine-tune pre-trained models.In this paper, we introduce a contrastive preference model that enhances the traditional Plackett-Luce model by incorporating an indicator function. Building upon this novel preference model, we propose Contrastive Preference Learning (CPL), which uses offline samples with list-wise preferences to fine-tune a pre-trained model in Neural Machine Translation. Our experiments, conducted on three language pairs, demonstrate that CPL outperforms not only the vanilla Transformer model but also other token-level and sequence-level baselines. Furthermore, the ablation study highlights the essential role of the proposed indicator function in achieving this improvement.</abstract>
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%0 Conference Proceedings
%T Contrastive Preference Learning for Neural Machine Translation
%A He, Jianfei
%A Sun, Shichao
%A Peng, Sen
%A Xu, Jie
%A Jia, Xiaohua
%A Li, Wenjie
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F he-etal-2024-contrastive
%X There exists a discrepancy between the token-level objective during training and the overall sequence-level quality that is expected from the model. This discrepancy leads to issues like exposure bias.To align the model with human expectations, sequence-level objectives are often used to fine-tune pre-trained models.In this paper, we introduce a contrastive preference model that enhances the traditional Plackett-Luce model by incorporating an indicator function. Building upon this novel preference model, we propose Contrastive Preference Learning (CPL), which uses offline samples with list-wise preferences to fine-tune a pre-trained model in Neural Machine Translation. Our experiments, conducted on three language pairs, demonstrate that CPL outperforms not only the vanilla Transformer model but also other token-level and sequence-level baselines. Furthermore, the ablation study highlights the essential role of the proposed indicator function in achieving this improvement.
%R 10.18653/v1/2024.findings-naacl.174
%U https://aclanthology.org/2024.findings-naacl.174
%U https://doi.org/10.18653/v1/2024.findings-naacl.174
%P 2723-2735

Markdown (Informal)

[Contrastive Preference Learning for Neural Machine Translation](https://aclanthology.org/2024.findings-naacl.174) (He et al., Findings 2024)

• Contrastive Preference Learning for Neural Machine Translation (He et al., Findings 2024)

ACL

• Jianfei He, Shichao Sun, Sen Peng, Jie Xu, Xiaohua Jia, and Wenjie Li. 2024. Contrastive Preference Learning for Neural Machine Translation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2723–2735, Mexico City, Mexico. Association for Computational Linguistics.