@inproceedings{sun-etal-2023-cl,
title = "{CL}-{QR}: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational {AI} Agents",
author = "Sun, Zhongkai and
Zhao, Zhengyang and
Lu, Sixing and
Ma, Chengyuan and
Liu, Xiaohu and
Fan, Xing and
Shen, Wei and
Guo, Chenlei",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.40",
doi = "10.18653/v1/2023.emnlp-industry.40",
pages = "423--431",
abstract = "The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri rely on accurate spoken language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding user{'}s intent from imperfect spoken recognition result. However, due to the scarcity of non-English QR labels, providing high-quality QR for non-English users still remains a challenge. This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance. The proposed work also proposes a Module-wise Mutually-supervised Feedback learning (MMF) algorithm to enable the continually self-improving of the CL-QR, which alleviates the lack of cross-lingual QR training data and enhances the delivery of high-quality reformulations learned in English for multilingual queries. Both offline evaluation and online A/B testing demonstrates the effectiveness of the proposed method.",
}
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<abstract>The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri rely on accurate spoken language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding user’s intent from imperfect spoken recognition result. However, due to the scarcity of non-English QR labels, providing high-quality QR for non-English users still remains a challenge. This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance. The proposed work also proposes a Module-wise Mutually-supervised Feedback learning (MMF) algorithm to enable the continually self-improving of the CL-QR, which alleviates the lack of cross-lingual QR training data and enhances the delivery of high-quality reformulations learned in English for multilingual queries. Both offline evaluation and online A/B testing demonstrates the effectiveness of the proposed method.</abstract>
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%0 Conference Proceedings
%T CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents
%A Sun, Zhongkai
%A Zhao, Zhengyang
%A Lu, Sixing
%A Ma, Chengyuan
%A Liu, Xiaohu
%A Fan, Xing
%A Shen, Wei
%A Guo, Chenlei
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sun-etal-2023-cl
%X The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri rely on accurate spoken language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding user’s intent from imperfect spoken recognition result. However, due to the scarcity of non-English QR labels, providing high-quality QR for non-English users still remains a challenge. This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance. The proposed work also proposes a Module-wise Mutually-supervised Feedback learning (MMF) algorithm to enable the continually self-improving of the CL-QR, which alleviates the lack of cross-lingual QR training data and enhances the delivery of high-quality reformulations learned in English for multilingual queries. Both offline evaluation and online A/B testing demonstrates the effectiveness of the proposed method.
%R 10.18653/v1/2023.emnlp-industry.40
%U https://aclanthology.org/2023.emnlp-industry.40
%U https://doi.org/10.18653/v1/2023.emnlp-industry.40
%P 423-431
Markdown (Informal)
[CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents](https://aclanthology.org/2023.emnlp-industry.40) (Sun et al., EMNLP 2023)
ACL