@inproceedings{liu-etal-2020-cross-lingual-spoken,
title = "Cross-lingual Spoken Language Understanding with Regularized Representation Alignment",
author = "Liu, Zihan and
Winata, Genta Indra and
Xu, Peng and
Lin, Zhaojiang and
Fung, Pascale",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.587/",
doi = "10.18653/v1/2020.emnlp-main.587",
pages = "7241--7251",
abstract = "Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal. To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource. First, we regularize the representation of user utterances based on their corresponding labels. Second, we regularize the latent variable model (Liu et al., 2019) by leveraging adversarial training to disentangle the latent variables. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios, and our model, trained on a few-shot setting with only 3{\%} of the target language training data, achieves comparable performance to the supervised training with all the training data."
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<abstract>Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal. To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource. First, we regularize the representation of user utterances based on their corresponding labels. Second, we regularize the latent variable model (Liu et al., 2019) by leveraging adversarial training to disentangle the latent variables. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios, and our model, trained on a few-shot setting with only 3% of the target language training data, achieves comparable performance to the supervised training with all the training data.</abstract>
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%0 Conference Proceedings
%T Cross-lingual Spoken Language Understanding with Regularized Representation Alignment
%A Liu, Zihan
%A Winata, Genta Indra
%A Xu, Peng
%A Lin, Zhaojiang
%A Fung, Pascale
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-cross-lingual-spoken
%X Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal. To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource. First, we regularize the representation of user utterances based on their corresponding labels. Second, we regularize the latent variable model (Liu et al., 2019) by leveraging adversarial training to disentangle the latent variables. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios, and our model, trained on a few-shot setting with only 3% of the target language training data, achieves comparable performance to the supervised training with all the training data.
%R 10.18653/v1/2020.emnlp-main.587
%U https://aclanthology.org/2020.emnlp-main.587/
%U https://doi.org/10.18653/v1/2020.emnlp-main.587
%P 7241-7251
Markdown (Informal)
[Cross-lingual Spoken Language Understanding with Regularized Representation Alignment](https://aclanthology.org/2020.emnlp-main.587/) (Liu et al., EMNLP 2020)
ACL