@inproceedings{dziri-etal-2019-augmenting,
    title = "Augmenting Neural Response Generation with Context-Aware Topical Attention",
    author = "Dziri, Nouha  and
      Kamalloo, Ehsan  and
      Mathewson, Kory  and
      Zaiane, Osmar",
    editor = "Chen, Yun-Nung  and
      Bedrax-Weiss, Tania  and
      Hakkani-Tur, Dilek  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Luong, Thang-Minh  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W19-4103/",
    doi = "10.18653/v1/W19-4103",
    pages = "18--31",
    abstract = "Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines."
}