Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders

Daria Romanovna Ledneva, Denis Pavlovich Kuznetsov

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.
Anthology ID:
2024.lrec-main.1208
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
13847–13860
Language:
URL:
https://aclanthology.org/2024.lrec-main.1208
DOI:
Bibkey:
Cite (ACL):
Daria Romanovna Ledneva and Denis Pavlovich Kuznetsov. 2024. Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13847–13860, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Reimagining Intent Prediction: Insights from Graph-Based Dialogue Modeling and Sentence Encoders (Ledneva & Kuznetsov, LREC-COLING 2024)
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