@inproceedings{xu-carpuat-2018-university,
    title = "The {U}niversity of {M}aryland`s {C}hinese-{E}nglish Neural Machine Translation Systems at {WMT}18",
    author = "Xu, Weijia  and
      Carpuat, Marine",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W18-6431/",
    doi = "10.18653/v1/W18-6431",
    pages = "535--540",
    abstract = "This paper describes the University of Maryland`s submission to the WMT 2018 Chinese{\ensuremath{\leftrightarrow}}English news translation tasks. Our systems are BPE-based self-attentional Transformer networks with parallel and backtranslated monolingual training data. Using ensembling and reranking, we improve over the Transformer baseline by +1.4 BLEU for Chinese{\textrightarrow}English and +3.97 BLEU for English{\textrightarrow}Chinese on \textit{newstest2017}. Our best systems reach BLEU scores of 24.4 for Chinese{\textrightarrow}English and 39.0 for English{\textrightarrow}Chinese on \textit{newstest2018}."
}