@inproceedings{liu-zhang-2020-corpora,
    title = "Corpora for Document-Level Neural Machine Translation",
    author = "Liu, Siyou  and
      Zhang, Xiaojun",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.lrec-1.466/",
    pages = "3775--3781",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "Instead of translating sentences in isolation, document-level machine translation aims to capture discourse dependencies across sentences by considering a document as a whole. In recent years, there have been more interests in modelling larger context for the state-of-the-art neural machine translation (NMT). Although various document-level NMT models have shown significant improvements, there nonetheless exist three main problems: 1) compared with sentence-level translation tasks, the data for training robust document-level models are relatively low-resourced; 2) experiments in previous work are conducted on their own datasets which vary in size, domain and language; 3) proposed approaches are implemented on distinct NMT architectures such as recurrent neural networks (RNNs) and self-attention networks (SANs). In this paper, we aims to alleviate the low-resource and under-universality problems for document-level NMT. First, we collect a large number of existing document-level corpora, which covers 7 language pairs and 6 domains. In order to address resource sparsity, we construct a novel document parallel corpus in Chinese-Portuguese, which is a non-English-centred and low-resourced language pair. Besides, we implement and evaluate the commonly-cited document-level method on top of the advanced Transformer model with universal settings. Finally, we not only demonstrate the effectiveness and universality of document-level NMT, but also release the preprocessed data, source code and trained models for comparison and reproducibility."
}