@inproceedings{panda-etal-2021-multilingual,
title = "Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems",
author = "Panda, Subhadarshi and
Tirkaz, Caglar and
Falke, Tobias and
Lehnen, Patrick",
editor = "Papangelis, Alexandros and
Budzianowski, Pawe{\l} and
Liu, Bing and
Nouri, Elnaz and
Rastogi, Abhinav and
Chen, Yun-Nung",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4convai-1.4",
doi = "10.18653/v1/2021.nlp4convai-1.4",
pages = "30--39",
abstract = "The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language. The generated utterances can be used to augment existing training data to improve intent classification and slot labeling models. We evaluate the quality of generated utterances using intrinsic evaluation metrics and by conducting downstream evaluation experiments with English as the source language and nine different target languages. Our method shows promise across languages, even in a zero-shot setting where no seed data is available.",
}
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%0 Conference Proceedings
%T Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems
%A Panda, Subhadarshi
%A Tirkaz, Caglar
%A Falke, Tobias
%A Lehnen, Patrick
%Y Papangelis, Alexandros
%Y Budzianowski, Paweł
%Y Liu, Bing
%Y Nouri, Elnaz
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%S Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F panda-etal-2021-multilingual
%X The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language. The generated utterances can be used to augment existing training data to improve intent classification and slot labeling models. We evaluate the quality of generated utterances using intrinsic evaluation metrics and by conducting downstream evaluation experiments with English as the source language and nine different target languages. Our method shows promise across languages, even in a zero-shot setting where no seed data is available.
%R 10.18653/v1/2021.nlp4convai-1.4
%U https://aclanthology.org/2021.nlp4convai-1.4
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.4
%P 30-39
Markdown (Informal)
[Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems](https://aclanthology.org/2021.nlp4convai-1.4) (Panda et al., NLP4ConvAI 2021)
ACL