Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark
Yuxing Long, Binyuan Hui, Caixia Yuan, Fei Huang, Yongbin Li, Xiaojie Wang
Abstract
Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with Subjective Preference), which contains 12K shopping dialogs in complex store scenes. The data is built in two phases with human annotations to ensure quality and diversity. SURE is well-annotated with subjective preferences and recommendation acts proposed by sales experts. A comprehensive analysis is given to reveal the distinguishing features of SURE. Three benchmark tasks are then proposed on the data to evaluate the capability of multimodal recommendation agents. Basing on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.- Anthology ID:
- 2023.findings-acl.217
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3515–3533
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.217
- DOI:
- 10.18653/v1/2023.findings-acl.217
- Cite (ACL):
- Yuxing Long, Binyuan Hui, Caixia Yuan, Fei Huang, Yongbin Li, and Xiaojie Wang. 2023. Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark. In Findings of the Association for Computational Linguistics: ACL 2023, pages 3515–3533, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark (Long et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.217.pdf