@inproceedings{ronnqvist-etal-2017-recurrent,
    title = "A Recurrent Neural Model with Attention for the Recognition of {C}hinese Implicit Discourse Relations",
    author = {R{\"o}nnqvist, Samuel  and
      Schenk, Niko  and
      Chiarcos, Christian},
    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/P17-2040/",
    doi = "10.18653/v1/P17-2040",
    pages = "256--262",
    abstract = "We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model`s ability to selectively focus on the relevant parts of an input sequence."
}