End-to-End Slot Alignment and Recognition for Cross-Lingual NLU

Weijia Xu, Batool Haider, Saab Mansour


Abstract
Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
Anthology ID:
2020.emnlp-main.410
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5052–5063
Language:
URL:
https://aclanthology.org/2020.emnlp-main.410
DOI:
10.18653/v1/2020.emnlp-main.410
Bibkey:
Cite (ACL):
Weijia Xu, Batool Haider, and Saab Mansour. 2020. End-to-End Slot Alignment and Recognition for Cross-Lingual NLU. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5052–5063, Online. Association for Computational Linguistics.
Cite (Informal):
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU (Xu et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.410.pdf
Optional supplementary material:
 2020.emnlp-main.410.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939164
Code
 amazon-research/multiatis +  additional community code