HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System

Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu


Abstract
Recently, many task-oriented dialogue systems need to serve users in different languages. However, it is time-consuming to collect enough data of each language for training. Thus, zero-shot adaptation of cross-lingual task-oriented dialog systems has been studied. Most of existing methods consider the word-level alignments to conduct two main tasks for task-oriented dialogue system, i.e., intent detection and slot filling, and they rarely explore the dependency relations among these two tasks. In this paper, we propose a hierarchical framework to classify the pre-defined intents in the high-level and fulfill slot filling under the guidance of intent in the low-level. Particularly, we incorporate sentence-level alignment among different languages to enhance the performance of intent detection. The extensive experiments report that our proposed method achieves the SOTA performance on a public task-oriented dialog dataset.
Anthology ID:
2022.coling-1.396
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:
4492–4498
Language:
URL:
https://aclanthology.org/2022.coling-1.396
DOI:
Bibkey:
Cite (ACL):
Zhanyu Ma, Jian Ye, Xurui Yang, and Jianfeng Liu. 2022. HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4492–4498, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (Ma et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.396.pdf