Character expression for spoken dialogue systems with semi-supervised learning using Variational Auto-Encoder

Character of spoken dialogue systems is important not only for giving a positive impression of the system but also for gaining rapport from users. We have proposed a character expression model for spoken dialogue systems. The model expresses three character traits (extroversion, emotional instability, and politeness) of spoken dialogue systems by controlling spoken dialogue behaviors: utterance amount, backchannel, filler, and switching pause length. One major problem in training this model is that it is costly and time-consuming to collect many pair data of character traits and behaviors. To address this problem, semi-supervised learning is proposed based on a variational auto-encoder that exploits both the limited amount of labeled pair data and unlabeled corpus data. It was confirmed that the proposed model can express given characters more accurately than a baseline model with only supervised learning. We also implemented the character expression model in a spoken dialogue system for an autonomous android robot, and then conducted a subjective experiment with 75 university students to confirm the effectiveness of the character expression for specific dialogue scenarios. The results showed that expressing a character in accordance with the dialogue task by the proposed model improves the user’s impression of the appropriateness in formal dialogue such as job interview

Can a robot laugh with you?: Shared laughter generation for empathetic spoken dialogue

人と一緒に笑う会話ロボットを開発 --人に共感し、人と共生する会話AIの実現に向けて--. 京都大学プレスリリース. 2022-09-29.Spoken dialogue systems must be able to express empathy to achieve natural interaction with human users. However, laughter generation requires a high level of dialogue understanding. Thus, implementing laughter in existing systems, such as in conversational robots, has been challenging. As a first step toward solving this problem, rather than generating laughter from user dialogue, we focus on “shared laughter, ” where a user laughs using either solo or speech laughs (initial laugh), and the system laughs in turn (response laugh). The proposed system consists of three models: 1) initial laugh detection, 2) shared laughter prediction, and 3) laugh type selection. We trained each model using a human-robot speed dating dialogue corpus. For the first model, a recurrent neural network was applied, and the detection performance achieved an F1 score of 82.6%. The second model used the acoustic and prosodic features of the initial laugh and achieved a prediction accuracy above that of the random prediction. The third model selects the type of system’s response laugh as social or mirthful laugh based on the same features of the initial laugh. We then implemented the full shared laughter generation system in an attentive listening dialogue system and conducted a dialogue listening experiment. The proposed system improved the impression of the dialogue system such as empathy perception compared to a naive baseline without laughter and a reactive system that always responded with only social laughs. We propose that our system can be used for situated robot interaction and also emphasize the need for integrating proper empathetic laughs into conversational robots and agents

Mining Coding Patterns to Detect Crosscutting Concerns in Java Programs

Reverse Engineering, 2008. WCRE '08. 15th Working Conference onDate of Conference:15-18 Oct. 2008Conference Location :Antwer

Mining Application-Specific Coding Patterns for Software Maintenance

LATE '08 Proceedings of the 2008 AOSD workshop on Linking aspect technology and evolutio

Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation

Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user's experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user's perspective, ignoring the system's perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user's perspective (user's desires and reactions) and the system's perspective (system's intentions and reactions). We enhance ChatGPT's ability to reason for the system's perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.Comment: Accepted by the 24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2023

Enzymatic hydrolyzing performance of Acremonium cellulolyticus and Trichoderma reesei against three lignocellulosic materials

<p>Abstract</p> <p>Background</p> <p>Bioethanol isolated from lignocellulosic biomass represents one of the most promising renewable and carbon neutral alternative liquid fuel sources. Enzymatic saccharification using cellulase has proven to be a useful method in the production of bioethanol. The filamentous fungi <it>Acremonium cellulolyticus </it>and <it>Trichoderma reesei </it>are known to be potential cellulase producers. In this study, we aimed to reveal the advantages and disadvantages of the cellulase enzymes derived from these fungi.</p> <p>Results</p> <p>We compared <it>A. cellulolyticus </it>and <it>T. reesei </it>cellulase activity against the three lignocellulosic materials: eucalyptus, Douglas fir and rice straw. Saccharification analysis using the supernatant from each culture demonstrated that the enzyme mixture derived from <it>A. cellulolyticus </it>exhibited 2-fold and 16-fold increases in Filter Paper enzyme and β-glucosidase specific activities, respectively, compared with that derived from <it>T. reesei</it>. In addition, culture supernatant from <it>A. cellulolyticus </it>produced glucose more rapidly from the lignocellulosic materials. Meanwhile, culture supernatant derived from <it>T. reesei </it>exhibited a 2-fold higher xylan-hydrolyzing activity and produced more xylose from eucalyptus (72% yield) and rice straw (43% yield). Although the commercial enzymes Acremonium cellulase (derived from <it>A. cellulolyticus</it>, Meiji Seika Co.) demonstrated a slightly lower cellulase specific activity than Accellerase 1000 (derived from <it>T. reesei</it>, Genencor), the glucose yield (over 65%) from lignocellulosic materials by Acremonium cellulase was higher than that of Accellerase 1000 (less than 60%). In addition, the mannan-hydrolyzing activity of Acremonium cellulase was 16-fold higher than that of Accellerase 1000, and the conversion of mannan to mannobiose and mannose by Acremonium cellulase was more efficient.</p> <p>Conclusion</p> <p>We investigated the hydrolysis of lignocellulosic materials by cellulase derived from two types of filamentous fungi. We found that glucan-hydrolyzing activity of the culture supernatant from <it>A. cellulolyticus </it>was superior to that from <it>T. reesei</it>, while the xylan-hydrolyzing activity was superior for the cellulase from <it>T. reesei</it>. Moreover, Acremonium cellulase exhibited a greater glucan and mannan-hydrolyzing activity than Accellerase 1000.</p

Requirements of basic amino acid residues within the lectin-like domain of LOX-1 for the binding of oxidized low-density lipoprotein

AbstractLectin-like OxLDL receptor-1 (LOX-1) was identified as the major receptor for oxidized low-density lipoprotein (OxLDL) in aortic endothelial cells. LOX-1 is a type II membrane protein that structurally belongs to the C-type lectin family. Here, we found that the lectin-like domain of LOX-1 is essential for ligand binding, but the neck domain is not. In particular, the large loop between the third and fourth cysteine of the lectin-like domain plays a critical role for OxLDL binding as well as C-terminal end residues. Alanine-directed mutagenesis of the basic amino acid residues around this region revealed that all of the basic residues are involved in OxLDL binding. Simultaneous mutations of these basic residues almost abolished the OxLDL-binding activity of LOX-1. Electrostatic interaction between basic residues in the lectin-like domain of LOX-1 and negatively charged OxLDL is critical for the binding activity of LOX-1

Towards Objective Evaluation of Socially-Situated Conversational Robots: Assessing Human-Likeness through Multimodal User Behaviors

This paper tackles the challenging task of evaluating socially situated conversational robots and presents a novel objective evaluation approach that relies on multimodal user behaviors. In this study, our main focus is on assessing the human-likeness of the robot as the primary evaluation metric. While previous research often relied on subjective evaluations from users, our approach aims to evaluate the robot's human-likeness based on observable user behaviors indirectly, thus enhancing objectivity and reproducibility. To begin, we created an annotated dataset of human-likeness scores, utilizing user behaviors found in an attentive listening dialogue corpus. We then conducted an analysis to determine the correlation between multimodal user behaviors and human-likeness scores, demonstrating the feasibility of our proposed behavior-based evaluation method.Comment: Accepted by 25th ACM International Conference on Multimodal Interaction (ICMI '23), Late-Breaking Result