GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding

Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan


Abstract
Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation effort. However, existing models solely rely on shared parameters, which can only perform implicit alignment across languages. We present Global-Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming. Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages. In addition, a key step in GL-CLeF is a proposed Local and Global component, which achieves a fine-grained cross-lingual transfer (i.e., sentence-level Local intent transfer, token-level Local slot transfer, and semantic-level Global transfer across intent and slot). Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and successfully pulls representations of similar sentences across languages closer.
Anthology ID:
2022.acl-long.191
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2677–2686
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.acl-long.191/
DOI:
10.18653/v1/2022.acl-long.191
Bibkey:
Cite (ACL):
Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, and Min-Yen Kan. 2022. GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2677–2686, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (Qin et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.acl-long.191.pdf
Software:
 2022.acl-long.191.software.zip
Code
 lightchen233/gl-clef