Exploration of multilingual prompts in document-grounded dialogue

Xiaocheng Zhang; Huang Qing; Fu Lin

doi:10.18653/v1/2023.dialdoc-1.3

Exploration of multilingual prompts in document-grounded dialogue

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Abstract

Transferring DGD models from high-resource languages to low-resource languages is a meaningful but challenging task. Being able to provide multilingual responses to multilingual documents further complicates the task. This paper describes our method at DialDoc23 Shared Task (Document-Grounded Dialogue and Conversational Question Answering) for generate responses based on the most relevant passage retrieved. We divide it into three steps of retrieval, re-ranking and generation. Our methods include negative sample augmentation, prompt learning, pseudo-labeling and ensemble. On the submission page, we rank 2nd based on the sum of token-level F1, SacreBleu and Rouge-L scores used for the final evaluation, and get the total score of 210.25.

Anthology ID:: 2023.dialdoc-1.3
Volume:: Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
Month:: July
Year:: 2023
Address:: Toronto, Canada
Editors:: Roee Aharoni, Nouha Dziri, Song Feng, Yongbin Li, Yu Li, Hui Wan
Venue:: dialdoc
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 30–35
Language:
URL:: https://aclanthology.org/2023.dialdoc-1.3/
DOI:: 10.18653/v1/2023.dialdoc-1.3
Bibkey:
Cite (ACL):: Xiaocheng Zhang, Huang Qing, and Fu Lin. 2023. Exploration of multilingual prompts in document-grounded dialogue. In Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 30–35, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):: Exploration of multilingual prompts in document-grounded dialogue (Zhang et al., dialdoc 2023)
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PDF:: https://aclanthology.org/2023.dialdoc-1.3.pdf

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@inproceedings{zhang-etal-2023-exploration,
    title = "Exploration of multilingual prompts in document-grounded dialogue",
    author = "Zhang, Xiaocheng  and
      Qing, Huang  and
      Lin, Fu",
    editor = "Aharoni, Roee  and
      Dziri, Nouha  and
      Feng, Song  and
      Li, Yongbin  and
      Li, Yu  and
      Wan, Hui",
    booktitle = "Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.dialdoc-1.3/",
    doi = "10.18653/v1/2023.dialdoc-1.3",
    pages = "30--35",
    abstract = "Transferring DGD models from high-resource languages to low-resource languages is a meaningful but challenging task. Being able to provide multilingual responses to multilingual documents further complicates the task. This paper describes our method at DialDoc23 Shared Task (Document-Grounded Dialogue and Conversational Question Answering) for generate responses based on the most relevant passage retrieved. We divide it into three steps of retrieval, re-ranking and generation. Our methods include negative sample augmentation, prompt learning, pseudo-labeling and ensemble. On the submission page, we rank 2nd based on the sum of token-level F1, SacreBleu and Rouge-L scores used for the final evaluation, and get the total score of 210.25."
}

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%0 Conference Proceedings
%T Exploration of multilingual prompts in document-grounded dialogue
%A Zhang, Xiaocheng
%A Qing, Huang
%A Lin, Fu
%Y Aharoni, Roee
%Y Dziri, Nouha
%Y Feng, Song
%Y Li, Yongbin
%Y Li, Yu
%Y Wan, Hui
%S Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-exploration
%X Transferring DGD models from high-resource languages to low-resource languages is a meaningful but challenging task. Being able to provide multilingual responses to multilingual documents further complicates the task. This paper describes our method at DialDoc23 Shared Task (Document-Grounded Dialogue and Conversational Question Answering) for generate responses based on the most relevant passage retrieved. We divide it into three steps of retrieval, re-ranking and generation. Our methods include negative sample augmentation, prompt learning, pseudo-labeling and ensemble. On the submission page, we rank 2nd based on the sum of token-level F1, SacreBleu and Rouge-L scores used for the final evaluation, and get the total score of 210.25.
%R 10.18653/v1/2023.dialdoc-1.3
%U https://aclanthology.org/2023.dialdoc-1.3/
%U https://doi.org/10.18653/v1/2023.dialdoc-1.3
%P 30-35

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Markdown (Informal)

[Exploration of multilingual prompts in document-grounded dialogue](https://aclanthology.org/2023.dialdoc-1.3/) (Zhang et al., dialdoc 2023)

Exploration of multilingual prompts in document-grounded dialogue (Zhang et al., dialdoc 2023)

ACL

Xiaocheng Zhang, Huang Qing, and Fu Lin. 2023. Exploration of multilingual prompts in document-grounded dialogue. In Proceedings of the Third DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, pages 30–35, Toronto, Canada. Association for Computational Linguistics.