Satoshi Kobashikawa


Multimodal Negotiation Corpus with Various Subjective Assessments for Social-Psychological Outcome Prediction from Non-Verbal Cues
Nobukatsu Hojo | Satoshi Kobashikawa | Saki Mizuno | Ryo Masumura
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This study investigates social-psychological negotiation-outcome prediction (SPNOP), a novel task for estimating various subjective evaluation scores of negotiation, such as satisfaction and trust, from negotiation dialogue data. To investigate SPNOP, a corpus with various psychological measurements is beneficial because the interaction process of negotiation relates to many aspects of psychology. However, current negotiation corpora only include information related to objective outcomes or a single aspect of psychology. In addition, most use the “laboratory setting” that uses non-skilled negotiators and over simplified negotiation scenarios. There is a concern that such a gap with actual negotiation will intrinsically affect the behavior and psychology of negotiators in the corpus, which can degrade the performance of models trained from the corpus in real situations. Therefore, we created a negotiation corpus with three features; 1) was assessed with various psychological measurements, 2) used skilled negotiators, and 3) used scenarios of context-rich negotiation. We recorded video and audio of negotiations in Japanese to investigate SPNOP in the context of social signal processing. Experimental results indicate that social-psychological outcomes can be effectively estimated from multimodal information.


Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
Itsumi Saito | Jun Suzuki | Kyosuke Nishida | Kugatsu Sadamitsu | Satoshi Kobashikawa | Ryo Masumura | Yuji Matsumoto | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models. Attention based encoder-decoder models are greatly effective in generating many natural languages. % such as machine translation or machine summarization. In general, we have to prepare for a large amount of training data to train an encoder-decoder model. Unlike machine translation, there are few training data for text-normalization tasks. In this paper, we propose two methods for generating augmented data. The experimental results with Japanese dialect normalization indicate that our methods are effective for an encoder-decoder model and achieve higher BLEU score than that of baselines. We also investigated the oracle performance and revealed that there is sufficient room for improving an encoder-decoder model.