@inproceedings{ding-etal-2017-visualizing,
    title = "Visualizing and Understanding Neural Machine Translation",
    author = "Ding, Yanzhuo  and
      Liu, Yang  and
      Luan, Huanbo  and
      Sun, Maosong",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P17-1106/",
    doi = "10.18653/v1/P17-1106",
    pages = "1150--1159",
    abstract = "While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors."
}