@inproceedings{dalvi-etal-2018-incremental,
    title = "Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation",
    author = "Dalvi, Fahim  and
      Durrani, Nadir  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/N18-2079/",
    doi = "10.18653/v1/N18-2079",
    pages = "493--499",
    abstract = "We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework."
}