@inproceedings{xu-etal-2021-caire,
title = "{CA}i{RE} in {D}ial{D}oc21: Data Augmentation for Information Seeking Dialogue System",
author = "Xu, Yan and
Ishii, Etsuko and
Winata, Genta Indra and
Lin, Zhaojiang and
Madotto, Andrea and
Liu, Zihan and
Xu, Peng and
Fung, Pascale",
editor = "Feng, Song and
Reddy, Siva and
Alikhani, Malihe and
He, He and
Ji, Yangfeng and
Iyyer, Mohit and
Yu, Zhou",
booktitle = "Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dialdoc-1.6",
doi = "10.18653/v1/2021.dialdoc-1.6",
pages = "46--51",
abstract = "Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users{'} needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.",
}
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<abstract>Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.</abstract>
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%0 Conference Proceedings
%T CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System
%A Xu, Yan
%A Ishii, Etsuko
%A Winata, Genta Indra
%A Lin, Zhaojiang
%A Madotto, Andrea
%A Liu, Zihan
%A Xu, Peng
%A Fung, Pascale
%Y Feng, Song
%Y Reddy, Siva
%Y Alikhani, Malihe
%Y He, He
%Y Ji, Yangfeng
%Y Iyyer, Mohit
%Y Yu, Zhou
%S Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F xu-etal-2021-caire
%X Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.
%R 10.18653/v1/2021.dialdoc-1.6
%U https://aclanthology.org/2021.dialdoc-1.6
%U https://doi.org/10.18653/v1/2021.dialdoc-1.6
%P 46-51
Markdown (Informal)
[CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System](https://aclanthology.org/2021.dialdoc-1.6) (Xu et al., dialdoc 2021)
ACL