When Crowd Meets Persona: Creating a Large-Scale Open-Domain Persona Dialogue Corpus
| Autor: | Cho, Won Ik, Lee, Yoon Kyung, Bae, Seoyeon, Kim, Jihwan, Park, Sangah, Kim, Moosung, Hahn, Sowon, Kim, Nam Soo |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | Building a natural language dataset requires caution since word semantics is vulnerable to subtle text change or the definition of the annotated concept. Such a tendency can be seen in generative tasks like question-answering and dialogue generation and also in tasks that create a categorization-based corpus, like topic classification or sentiment analysis. Open-domain conversations involve two or more crowdworkers freely conversing about any topic, and collecting such data is particularly difficult for two reasons: 1) the dataset should be ``crafted" rather than ``obtained" due to privacy concerns, and 2) paid creation of such dialogues may differ from how crowdworkers behave in real-world settings. In this study, we tackle these issues when creating a large-scale open-domain persona dialogue corpus, where persona implies that the conversation is performed by several actors with a fixed persona and user-side workers from an unspecified crowd. Comment: Presented at HCOMP 2022 as Works-in-Progress |
| Databáze: | arXiv |
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