@inproceedings{jin-etal-2021-towards,
title = "Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems",
author = "Jin, Di and
Gao, Shuyang and
Kim, Seokhwan and
Liu, Yang and
Hakkani-Tur, Dilek",
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-1.27",
doi = "10.18653/v1/2021.nlp4convai-1.27",
pages = "281--288",
abstract = "Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task mainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero-shot cases, and quickly learns a high-performing detector with only a few shots by updating less than 3K parameters. We demonstrate REDE{'}s competitive performance on DSTC9 data and our newly collected test set.",
}
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<abstract>Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task mainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero-shot cases, and quickly learns a high-performing detector with only a few shots by updating less than 3K parameters. We demonstrate REDE’s competitive performance on DSTC9 data and our newly collected test set.</abstract>
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%0 Conference Proceedings
%T Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
%A Jin, Di
%A Gao, Shuyang
%A Kim, Seokhwan
%A Liu, Yang
%A Hakkani-Tur, Dilek
%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 jin-etal-2021-towards
%X Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focuses on identifying such user requests. Existing methods for this task mainly rely on fine-tuning pre-trained models on large annotated data. We propose a novel method, REDE, based on adaptive representation learning and density estimation. REDE can be applied to zero-shot cases, and quickly learns a high-performing detector with only a few shots by updating less than 3K parameters. We demonstrate REDE’s competitive performance on DSTC9 data and our newly collected test set.
%R 10.18653/v1/2021.nlp4convai-1.27
%U https://aclanthology.org/2021.nlp4convai-1.27
%U https://doi.org/10.18653/v1/2021.nlp4convai-1.27
%P 281-288
Markdown (Informal)
[Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems](https://aclanthology.org/2021.nlp4convai-1.27) (Jin et al., NLP4ConvAI 2021)
ACL