Attention mechanisms have played a crucial role in the success of Transformer models, as seen in platforms like ChatGPT. However, since they compute attentions from relationships between only one or two object types, they fail to effectively capture multi-object relationships in real-world scenarios, resulting in low prediction accuracy. In fact, they cannot calculate attention weights among diverse object types, such as the `comments,' `replies,' and `subjects' that naturally constitute conversations on platforms like Reddit or X, representing relationships simultaneously observed in real-world contexts. To overcome this limitation, we introduce the Tensorized Attention Model (TAM), which uses the Tucker decomposition to calculate attention weights across various object types and seamlessly integrates them into the Transformer models. Evaluations show that TAM significantly outperforms existing encoder methods, and its integration into the LoRA adapter for Llama2 enhances fine-tuning accuracy.

Facilitating self-disclosure without causing discomfort remains a difficult task—especially for AI systems. In real-world applications such as career counseling, wellbeing support, and onboarding interviews, eliciting personal information like concerns, goals, and personality traits is essential. However, asking such questions directly often leads to discomfort and disengagement. We address this issue with RaPSIL (Rapport-aware Preference-guided Self-disclosure Interview Learner), a two-stage LLM-based system that fosters natural, engaging conversations to promote self-disclosure. In the first stage, RaPSIL selectively imitates interviewer utterances that have been evaluated by LLMs for both strategic effectiveness and social sensitivity. It leverages LLMs as multi-perspective judges in this selection process. In the second stage, it conducts self-play simulations, using the Reflexion framework to analyze failures and expand a database with both successful and problematic utterances. This dual learning process allows RaPSIL to go beyond simple imitation, improving its ability to handle sensitive topics naturally by learning from both successful and failed utterances. In a comprehensive evaluation with real users, RaPSIL outperformed baselines in enjoyability, warmth, and willingness to re-engage, while also capturing self-descriptions more accurately. Notably, its impression scores remained stable even during prolonged interactions, demonstrating its ability to balance rapport building with effective information elicitation. These results show that RaPSIL enables socially aware AI interviewers capable of eliciting sensitive personal information while maintaining user trust and comfort—an essential capability for real-world dialogue systems.

Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective information contained in dialogues on group performance. We hypothesize that the more subjective information interlocutors exchange, the better the group performance in collaborative work. We collected dialogues between drivers and engineers in motorsports when deciding how the car should be tuned as a suitable case to verify this hypothesis. Our analysis suggests that the greater the amount of subjective information (which we defined as “sensation”) in the driver’s utterances, the greater the race performance and driver satisfaction with the car’s tuning. The results indicate that it is essential for the development of dialogue research to create a corpus of situations that require high performance through collaboration among experts with different backgrounds but who have mastered their respective fields.

Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.

In recent years, Unicode has been standardized; and with the penetration of SNSs, the use of emojis has become common. Emojis, as they are also known, are most efective in expressing emotions in sentences. Sentiment analysis in natural language processing so far has involved learning by manual labeling of sentences. By using suitable emojis estimated from sentences, people might express their emotions more clearly and laconically. In this paper, we propose a new model that learns from sentences using emojis as labels, collecting Japanese tweets from Twitter as the corpus. We verify and compare multiple models based on EncoderDecoder Model of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our sophisticated experiments demonstrate that emojis are efective in expressing tweet emotion.