@inproceedings{menglong-yanliang-2024-simclnmt,
    title = "{S}im{CLNMT}: A Simple Contrastive Learning Method for Enhancing Neural Machine Translation Quality",
    author = "Menglong, Xu  and
      Yanliang, Zhang",
    editor = "Sun, Maosong  and
      Liang, Jiye  and
      Han, Xianpei  and
      Liu, Zhiyuan  and
      He, Yulan",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.ccl-1.81/",
    pages = "1047--1058",
    language = "eng",
    abstract = "``Neural Machine Translation (NMT) models are typically trained using Maximum LikelihoodEstimation (MLE). However, this approach has a limitation: while it might select the bestword for the immediate context, it does not generally optimize for the entire sentence. Tomitigate this issue, we propose a simple yet effective training method called SimCLNMT.This method is designed to select words that fit well in the immediate context and also en-hance the overall translation quality over time. During training, SimCLNMT scores multiplesystem-generated (candidate) translations using the logarithm of conditional probabilities.Itthen employs a ranking loss function to learn and adjust these probabilities to align with thecorresponding quality scores. Our experimental results demonstrate that SimCLNMT consis-tently outperforms traditional MLE training on both the NIST English-Chinese and WMT{'}14English-German datasets. Further analysis also indicates that the translations generated by ourmodel are more closely aligned with the corresponding quality scores. We release our code athttps://github.com/chaos130/fairseq{\_}SimCLNMT.Introduction''"
}