@inproceedings{chen-etal-2021-building,
title = "Building Goal-oriented Document-grounded Dialogue Systems",
author = "Chen, Xi and
Lin, Faner and
Zhou, Yeju and
Ma, Kaixin and
Francis, Jonathan and
Nyberg, Eric and
Oltramari, Alessandro",
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-1.14",
doi = "10.18653/v1/2021.dialdoc-1.14",
pages = "109--112",
abstract = "In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation. We proposed several pre-processing and post-processing methods, and we experimented with data augmentation by pre-training the models on other relevant datasets. Our best model for knowledge identification outperformed the baseline by 10.5+ f1-score on the test-dev split, and our best model for response generation outperformed the baseline by 11+ Sacrebleu score on the test-dev split.",
}
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<abstract>In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation. We proposed several pre-processing and post-processing methods, and we experimented with data augmentation by pre-training the models on other relevant datasets. Our best model for knowledge identification outperformed the baseline by 10.5+ f1-score on the test-dev split, and our best model for response generation outperformed the baseline by 11+ Sacrebleu score on the test-dev split.</abstract>
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%0 Conference Proceedings
%T Building Goal-oriented Document-grounded Dialogue Systems
%A Chen, Xi
%A Lin, Faner
%A Zhou, Yeju
%A Ma, Kaixin
%A Francis, Jonathan
%A Nyberg, Eric
%A Oltramari, Alessandro
%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 chen-etal-2021-building
%X In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation. We proposed several pre-processing and post-processing methods, and we experimented with data augmentation by pre-training the models on other relevant datasets. Our best model for knowledge identification outperformed the baseline by 10.5+ f1-score on the test-dev split, and our best model for response generation outperformed the baseline by 11+ Sacrebleu score on the test-dev split.
%R 10.18653/v1/2021.dialdoc-1.14
%U https://aclanthology.org/2021.dialdoc-1.14
%U https://doi.org/10.18653/v1/2021.dialdoc-1.14
%P 109-112
Markdown (Informal)
[Building Goal-oriented Document-grounded Dialogue Systems](https://aclanthology.org/2021.dialdoc-1.14) (Chen et al., dialdoc 2021)
ACL
- Xi Chen, Faner Lin, Yeju Zhou, Kaixin Ma, Jonathan Francis, Eric Nyberg, and Alessandro Oltramari. 2021. Building Goal-oriented Document-grounded Dialogue Systems. In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021), pages 109–112, Online. Association for Computational Linguistics.