@inproceedings{wang-etal-2017-hybrid,
    title = "Hybrid Neural Network Alignment and Lexicon Model in Direct {HMM} for Statistical Machine Translation",
    author = "Wang, Weiyue  and
      Alkhouli, Tamer  and
      Zhu, Derui  and
      Ney, Hermann",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P17-2020/",
    doi = "10.18653/v1/P17-2020",
    pages = "125--131",
    abstract = "Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0{\%} Bleu scores on two different translation tasks."
}