@inproceedings{ledneva-kuznetsov-2024-reimagining,
    title = "Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders",
    author = "Ledneva, Daria Romanovna  and
      Kuznetsov, Denis Pavlovich",
    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.1208",
    pages = "13847--13860",
    abstract = "This paper presents a innovative approach tailored to the specific characteristics of closed-domain dialogue systems. Leveraging scenario dialog graphs, our method effectively addresses the challenges posed by highly specialized fields, where context comprehension is of paramount importance. By modeling dialogues as sequences of transitions between intents, representing distinct goals or requests, our approach focuses on accurate intent prediction for generating contextually relevant responses. The study conducts a thorough evaluation, comparing the performance of state-of-the-art sentence encoders in conjunction with graph-based models across diverse datasets encompassing both open and closed domains. The results highlight the superiority of our methodology, offering fresh perspectives on the integration of advanced sentence encoders and graph models for precise and contextually-driven intent prediction in dialogue systems. Additionally, the use of this approach enhances the transparency of generated output, enabling a deeper understanding of the reasoning behind system responses. This study significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches.",
}

This paper presents a innovative approach tailored to the specific characteristics of closed-domain dialogue systems. Leveraging scenario dialog graphs, our method effectively addresses the challenges posed by highly specialized fields, where context comprehension is of paramount importance. By modeling dialogues as sequences of transitions between intents, representing distinct goals or requests, our approach focuses on accurate intent prediction for generating contextually relevant responses. The study conducts a thorough evaluation, comparing the performance of state-of-the-art sentence encoders in conjunction with graph-based models across diverse datasets encompassing both open and closed domains. The results highlight the superiority of our methodology, offering fresh perspectives on the integration of advanced sentence encoders and graph models for precise and contextually-driven intent prediction in dialogue systems. Additionally, the use of this approach enhances the transparency of generated output, enabling a deeper understanding of the reasoning behind system responses. This study significantly advances the field of dialogue systems, providing valuable insights into the effectiveness and potential limitations of the proposed approaches.
Ledneva, Daria Romanovna Kuznetsov, Denis Pavlovich
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders