CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
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| Yayımlandı: | Transactions of the Association for Computational Linguistics vol. 8 (2020), p. 281 |
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| Yazar: | |
| Diğer Yazarlar: | , , , |
| Baskı/Yayın Bilgisi: |
MIT Press Journals, The
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| Konular: | |
| Online Erişim: | Citation/Abstract Full Text - PDF |
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| 001 | 2893885784 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2307-387X | ||
| 024 | 7 | |a 10.1162/tacl_a_00314 |2 doi | |
| 035 | |a 2893885784 | ||
| 045 | 2 | |b d20200101 |b d20201231 | |
| 100 | 1 | |a Zhu, Qi | |
| 245 | 1 | |a CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset | |
| 260 | |b MIT Press Journals, The |c 2020 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc. | |
| 653 | |a Simulation | ||
| 653 | |a Datasets | ||
| 653 | |a Human-computer interaction | ||
| 653 | |a Annotations | ||
| 653 | |a Dialogue | ||
| 653 | |a Modelling | ||
| 653 | |a Computational linguistics | ||
| 653 | |a Linguistics | ||
| 653 | |a Interactive computer systems | ||
| 653 | |a Hotels & motels | ||
| 653 | |a Natural language | ||
| 653 | |a Utterances | ||
| 653 | |a Models | ||
| 653 | |a Tracking | ||
| 653 | |a Restaurants | ||
| 653 | |a Dependency | ||
| 700 | 1 | |a Huang, Kaili | |
| 700 | 1 | |a Zhang, Zheng | |
| 700 | 1 | |a Zhu, Xiaoyan | |
| 700 | 1 | |a Huang, Minlie | |
| 773 | 0 | |t Transactions of the Association for Computational Linguistics |g vol. 8 (2020), p. 281 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2893885784/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2893885784/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |