@inproceedings{daheim-etal-2021-cascaded,
title = "Cascaded Span Extraction and Response Generation for Document-Grounded Dialog",
author = "Daheim, Nico and
Thulke, David and
Dugast, Christian and
Ney, Hermann",
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-1.8",
doi = "10.18653/v1/2021.dialdoc-1.8",
pages = "57--62",
abstract = "This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second sub-task, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline.",
}
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<abstract>This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second sub-task, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline.</abstract>
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%0 Conference Proceedings
%T Cascaded Span Extraction and Response Generation for Document-Grounded Dialog
%A Daheim, Nico
%A Thulke, David
%A Dugast, Christian
%A Ney, Hermann
%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 daheim-etal-2021-cascaded
%X This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second sub-task, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline.
%R 10.18653/v1/2021.dialdoc-1.8
%U https://aclanthology.org/2021.dialdoc-1.8
%U https://doi.org/10.18653/v1/2021.dialdoc-1.8
%P 57-62
Markdown (Informal)
[Cascaded Span Extraction and Response Generation for Document-Grounded Dialog](https://aclanthology.org/2021.dialdoc-1.8) (Daheim et al., dialdoc 2021)
ACL