@inproceedings{liu-etal-2022-information,
title = "Information Extraction and Human-Robot Dialogue towards Real-life Tasks A Baseline Study with the {M}obile{CS} Dataset",
author = "Liu, Hong and
Peng, Hao and
Ou, Zhijian and
Li, Juanzi and
Huang, Yi and
Feng, Junlan",
editor = "Ou, Zhijian and
Feng, Junlan and
Li, Juanzi",
booktitle = "Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)",
month = dec,
year = "2022",
address = "Abu Dhabi, Beijing (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.seretod-1.9",
doi = "10.18653/v1/2022.seretod-1.9",
pages = "75--84",
abstract = "Recently, there have merged a class of taskoriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual. Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customerservice staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset. We introduce how the two baselines are constructed, the problems encountered, and the results. We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.",
}
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<abstract>Recently, there have merged a class of taskoriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual. Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customerservice staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset. We introduce how the two baselines are constructed, the problems encountered, and the results. We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.</abstract>
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%0 Conference Proceedings
%T Information Extraction and Human-Robot Dialogue towards Real-life Tasks A Baseline Study with the MobileCS Dataset
%A Liu, Hong
%A Peng, Hao
%A Ou, Zhijian
%A Li, Juanzi
%A Huang, Yi
%A Feng, Junlan
%Y Ou, Zhijian
%Y Feng, Junlan
%Y Li, Juanzi
%S Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, Beijing (Hybrid)
%F liu-etal-2022-information
%X Recently, there have merged a class of taskoriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated data and thus are fundamentally different from real-life conversations, which are more noisy and casual. Recently, the SereTOD challenge is organized and releases the MobileCS dataset, which consists of real-world dialog transcripts between real users and customerservice staffs from China Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not only evaluating the construction of the dialogue system itself, but also examining information extraction from dialog transcripts, which is crucial for building the knowledge base for TOD. This paper mainly presents a baseline study of the two tasks with the MobileCS dataset. We introduce how the two baselines are constructed, the problems encountered, and the results. We anticipate that the baselines can facilitate exciting future research to build human-robot dialogue systems for real-life tasks.
%R 10.18653/v1/2022.seretod-1.9
%U https://aclanthology.org/2022.seretod-1.9
%U https://doi.org/10.18653/v1/2022.seretod-1.9
%P 75-84
Markdown (Informal)
[Information Extraction and Human-Robot Dialogue towards Real-life Tasks A Baseline Study with the MobileCS Dataset](https://aclanthology.org/2022.seretod-1.9) (Liu et al., SereTOD 2022)
ACL