@inproceedings{feng-etal-2021-multidoc2dial,
title = "{M}ulti{D}oc2{D}ial: Modeling Dialogues Grounded in Multiple Documents",
author = "Feng, Song and
Patel, Siva Sankalp and
Wan, Hui and
Joshi, Sachindra",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.498",
doi = "10.18653/v1/2021.emnlp-main.498",
pages = "6162--6176",
abstract = "We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.",
}
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<abstract>We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.</abstract>
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%0 Conference Proceedings
%T MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
%A Feng, Song
%A Patel, Siva Sankalp
%A Wan, Hui
%A Joshi, Sachindra
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F feng-etal-2021-multidoc2dial
%X We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.
%R 10.18653/v1/2021.emnlp-main.498
%U https://aclanthology.org/2021.emnlp-main.498
%U https://doi.org/10.18653/v1/2021.emnlp-main.498
%P 6162-6176
Markdown (Informal)
[MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents](https://aclanthology.org/2021.emnlp-main.498) (Feng et al., EMNLP 2021)
ACL
- Song Feng, Siva Sankalp Patel, Hui Wan, and Sachindra Joshi. 2021. MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6162–6176, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.