Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling

Yangjun Wu, Han Wang, Dongxiang Zhang, Gang Chen, Hao Zhang


Abstract
The joint multiple Intent Detection (ID) and Slot Filling (SF) is a significant challenge in spoken language understanding. Because the slots in an utterance may relate to multi-intents, most existing approaches focus on utilizing task-specific components to capture the relations between intents and slots. The customized networks restrict models from modeling commonalities between tasks and generalization for broader applications. To address the above issue, we propose a Unified Generative framework (UGEN) based on a prompt-based paradigm, and formulate the task as a question-answering problem. Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance. Through the instructional prompts, UGEN is guided to understand intents, slots, and their implicit correlations. On two popular multi-intent benchmark datasets, experimental results demonstrate that UGEN achieves new SOTA performances on full-data and surpasses the baselines by a large margin on 5-shot (28.1%) and 10-shot (23%) scenarios, which verify that UGEN is robust and effective.
Anthology ID:
2022.coling-1.631
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
7203–7208
Language:
URL:
https://aclanthology.org/2022.coling-1.631
DOI:
Bibkey:
Cite (ACL):
Yangjun Wu, Han Wang, Dongxiang Zhang, Gang Chen, and Hao Zhang. 2022. Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7203–7208, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling (Wu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.631.pdf
Code
 young1993/ugen
Data
ATISMixATISMixSNIPs