Abstract
Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a <dialogue act, topic> pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.- Anthology ID:
- 2023.emnlp-main.72
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1132–1143
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.72
- DOI:
- 10.18653/v1/2023.emnlp-main.72
- Cite (ACL):
- Jian Wang, Yi Cheng, Dongding Lin, Chak Leong, and Wenjie Li. 2023. Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1132–1143, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation (Wang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.emnlp-main.72.pdf