Abstract
In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user’s needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems. We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains. We discuss the current state of the research in SF and IC, and highlight challenges that still require attention.- Anthology ID:
- 2020.coling-main.42
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 480–496
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.42
- DOI:
- 10.18653/v1/2020.coling-main.42
- Cite (ACL):
- Samuel Louvan and Bernardo Magnini. 2020. Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey. In Proceedings of the 28th International Conference on Computational Linguistics, pages 480–496, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (Louvan & Magnini, COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.42.pdf
- Data
- ATIS, SNIPS