@inproceedings{tu-etal-2024-multi,
    title = "Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems",
    author = "Tu, Quan  and
      Tao, Chongyang  and
      Yan, Rui",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1026",
    pages = "11756--11765",
    abstract = "Retrieval-based dialogue agents aim at selecting a proper response according to multi-turn conversational history. Existing methods have achieved great progress in terms of retrieval accuracy on benchmarks with pre-trained language models. However, these methods simply concatenate all turns in the dialogue history as the input, ignoring the dialogue dependency and structural information between the utterances. Besides, they usually reason the relationship of the context-response pair at a single level of abstraction (e.g., utterance level), which can not comprehensively capture the fine-grained relation between the context and response. In this paper, we present the multi-grained conversational graph network (MCGN) that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. Evaluation results on two benchmarks indicate that the proposed multi-grained conversational graph network is helpful for dialogue context understanding and can bring consistent and significant improvement over the state-of-the-art methods.",
}

Retrieval-based dialogue agents aim at selecting a proper response according to multi-turn conversational history. Existing methods have achieved great progress in terms of retrieval accuracy on benchmarks with pre-trained language models. However, these methods simply concatenate all turns in the dialogue history as the input, ignoring the dialogue dependency and structural information between the utterances. Besides, they usually reason the relationship of the context-response pair at a single level of abstraction (e.g., utterance level), which can not comprehensively capture the fine-grained relation between the context and response. In this paper, we present the multi-grained conversational graph network (MCGN) that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. Evaluation results on two benchmarks indicate that the proposed multi-grained conversational graph network is helpful for dialogue context understanding and can bring consistent and significant improvement over the state-of-the-art methods.
Tu, Quan Tao, Chongyang Yan, Rui
Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems