Papers by Seiya Kawano
J-CRe3: A Japanese Conversation Dataset for Real-world Reference Resolution (2024.lrec-main)
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Nobuhiro Ueda, Hideko Habe, Akishige Yuguchi, Seiya Kawano, Yasutomo Kawanishi, Sadao Kurohashi, Koichiro Yoshino
| Challenge: | Existing studies have ground referential expressions in language to real-world objects for cooperative action generation. |
| Approach: | They propose a Japanese Conversation dataset for real-world reference resolution that ground referential expressions to visual information observed in egocentric views. |
| Outcome: | The proposed dataset contains egocentric video and dialogue audio of real-world conversations between two people acting as a master and assistant robot at home. |
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions (2024.lrec-main)
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| Challenge: | Visual question answering (VQA) tasks are often based on directives, which can cause ambiguities in human utterances. |
| Approach: | They propose a method that clarifies ambiguous questions using gaze information . they propose combining gaze information with gaze information to improve accuracy . |
| Outcome: | The proposed method improves performance in some cases of a GazeVQA system on Gaze. |
Analysis of Style-Shifting on Social Media: Using Neural Language Model Conditioned by Social Meanings (2023.findings-emnlp)
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Seiya Kawano, Shota Kanezaki, Angel Fernando Garcia Contreras, Akishige Yuguchi, Marie Katsurai, Koichiro Yoshino
| Challenge: | Using a personalized neural language model, we predict an individual’s conversational style based on surprisals predicted by a personal neural language modeling model. |
| Approach: | They propose a personalized neural language model that predicts changes in an individual’s conversational style based on surprisals predicted by a neural language modeling model. |
| Outcome: | The proposed model outperforms existing models in predicting conversational style-shifting in a test set and shows correlations between it and various conversation factors as well as human evaluation of style- shifting. |