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- W3102260352 abstract "In real-world dialogue, first-person visual information about where the other speakers are and what they are paying attention to is crucial to understand their intentions. Non-verbal responses also play an important role in social interactions. In this paper, we propose a visually-grounded first-person dialogue (VFD) dataset with verbal and non-verbal responses. The VFD dataset provides manually annotated (1) first-person images of agents, (2) utterances of human speakers, (3) eye-gaze locations of the speakers, and (4) the agents' verbal and non-verbal responses. We present experimental results obtained using the proposed VFD dataset and recent neural network models (e.g., BERT, ResNet). The results demonstrate that first-person vision helps neural network models correctly understand human intentions, and the production of non-verbal responses is a challenging task like that of verbal responses. Our dataset is publicly available." @default.
- W3102260352 created "2020-11-23" @default.
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- W3102260352 date "2020-01-01" @default.
- W3102260352 modified "2023-10-04" @default.
- W3102260352 title "A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses" @default.
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- W3102260352 doi "https://doi.org/10.18653/v1/2020.emnlp-main.267" @default.
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