@inproceedings{lu-etal-2020-chime,
    title = "{CHIME}: Cross-passage Hierarchical Memory Network for Generative Review Question Answering",
    author = "Lu, Junru  and
      Pergola, Gabriele  and
      Gui, Lin  and
      Li, Binyang  and
      He, Yulan",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.coling-main.229/",
    doi = "10.18653/v1/2020.coling-main.229",
    pages = "2547--2560",
    abstract = "We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module."
}