The demand for multimodal dialogue systems has been rising in various domains, emphasizing the importance of interpreting multimodal inputs from conversational and situational contexts. We explore three methods to tackle this problem and evaluate them on the largest situated dialogue dataset, SIMMC 2.1. Our best method, scene-dialogue alignment, improves the performance by ~20% F1-score compared to the SIMMC 2.1 baselines. We provide analysis and discussion regarding the limitation of our methods and the potential directions for future works. Our code is publicly available at https://github.com/holylovenia/multimodal-object-identification.
Which One Are You Referring To? Multimodal Object Identification in Situated Dialogue
A study on multimodal dialogue systems evaluates three methods, with scene-dialogue alignment showing a significant performance boost on the SIMMC 2.1 dataset.
- Year
- 2023
- Venue
- arXiv 2023
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- 3
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- arxiv.org/abs/2302.14680v2ARXIV-DEFAULT
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