@inproceedings{fu-etal-2022-doc2bot,
title = "{D}oc2{B}ot: Accessing Heterogeneous Documents via Conversational Bots",
author = "Fu, Haomin and
Zhang, Yeqin and
Yu, Haiyang and
Sun, Jian and
Huang, Fei and
Si, Luo and
Li, Yongbin and
Nguyen, Cam Tu",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.131",
doi = "10.18653/v1/2022.findings-emnlp.131",
pages = "1820--1836",
abstract = "This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations. This is of particular interest for companies and organizations that own a large number of manuals or instruction books. Despite its potential, the nature of our task poses several challenges: (1) documents contain various structures that hinder the ability of machines to comprehend, and (2) user information needs are often underspecified. Compared to prior datasets that either focus on a single structural type or overlook the role of questioning to uncover user needs, the Doc2Bot dataset is developed to target such challenges systematically. Our dataset contains over 100,000 turns based on Chinese documents from five domains, larger than any prior document-grounded dialog dataset for information seeking. We propose three tasks in Doc2Bot: (1) dialog state tracking to track user intentions, (2) dialog policy learning to plan system actions and contents, and (3) response generation which generates responses based on the outputs of the dialog policy. Baseline methods based on the latest deep learning models are presented, indicating that our proposed tasks are challenging and worthy of further research.",
}
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<abstract>This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations. This is of particular interest for companies and organizations that own a large number of manuals or instruction books. Despite its potential, the nature of our task poses several challenges: (1) documents contain various structures that hinder the ability of machines to comprehend, and (2) user information needs are often underspecified. Compared to prior datasets that either focus on a single structural type or overlook the role of questioning to uncover user needs, the Doc2Bot dataset is developed to target such challenges systematically. Our dataset contains over 100,000 turns based on Chinese documents from five domains, larger than any prior document-grounded dialog dataset for information seeking. We propose three tasks in Doc2Bot: (1) dialog state tracking to track user intentions, (2) dialog policy learning to plan system actions and contents, and (3) response generation which generates responses based on the outputs of the dialog policy. Baseline methods based on the latest deep learning models are presented, indicating that our proposed tasks are challenging and worthy of further research.</abstract>
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%0 Conference Proceedings
%T Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
%A Fu, Haomin
%A Zhang, Yeqin
%A Yu, Haiyang
%A Sun, Jian
%A Huang, Fei
%A Si, Luo
%A Li, Yongbin
%A Nguyen, Cam Tu
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F fu-etal-2022-doc2bot
%X This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations. This is of particular interest for companies and organizations that own a large number of manuals or instruction books. Despite its potential, the nature of our task poses several challenges: (1) documents contain various structures that hinder the ability of machines to comprehend, and (2) user information needs are often underspecified. Compared to prior datasets that either focus on a single structural type or overlook the role of questioning to uncover user needs, the Doc2Bot dataset is developed to target such challenges systematically. Our dataset contains over 100,000 turns based on Chinese documents from five domains, larger than any prior document-grounded dialog dataset for information seeking. We propose three tasks in Doc2Bot: (1) dialog state tracking to track user intentions, (2) dialog policy learning to plan system actions and contents, and (3) response generation which generates responses based on the outputs of the dialog policy. Baseline methods based on the latest deep learning models are presented, indicating that our proposed tasks are challenging and worthy of further research.
%R 10.18653/v1/2022.findings-emnlp.131
%U https://aclanthology.org/2022.findings-emnlp.131
%U https://doi.org/10.18653/v1/2022.findings-emnlp.131
%P 1820-1836
Markdown (Informal)
[Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots](https://aclanthology.org/2022.findings-emnlp.131) (Fu et al., Findings 2022)
ACL
- Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, and Cam Tu Nguyen. 2022. Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1820–1836, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.