Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue
Huazhong University of Science and Technology（华中科技大学）；Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)（华中科技大学与平安财产保险研究联合实验室）；Beijing Institute of Technology（北京理工大学）；Ping An Property & Casualty Insurance Company of China, Ltd（中国平安财产保险股份有限公司）
The core of the dialogue system is to generate relevant, informative, and human-like responses based on extensive dialogue history. Recently, dialogue generation domain has seen mainstream adoption of large language models (LLMs), due to its powerful capability in generating utterances. However, there is a natural deficiency for such models, that is, inherent position bias, which may lead them to pay more attention to the nearby utterances instead of causally relevant ones, resulting in generating irrelevant and generic responses in long-term dialogue. To alleviate such problem, in this paper, we propose a novel method, named Causal Perception long-term Dialogue framework (CPD), which employs perturbation-based causal variable discovery method to extract casually relevant utterances from the dialogue history and enhances model causal perception during fine-tuning. Specifically, a local-position awareness method is proposed in CPD for inter-sentence position correlation elimination, which helps models extract causally relevant utterances based on perturbations. Then, a casual-perception fine-tuning strategy is also proposed, to enhance the capability of discovering the causal invariant factors, by differently perturbing causally relevant and non-casually relevant ones for response generation. Experimental results on two datasets prove that our proposed method can effectively alleviate the position bias for multiple LLMs and achieve significant progress compared with existing baselines.
The blocks world is a classic toy domain that has long been used to build and test spatial reasoning systems. Despite its relative simplicity, tackling this domain in its full complexity requires the agent to exhibit a rich set of functional capabilities, ranging from vision to natural language understanding. There is currently a resurgence of interest in solving problems in such limited domains using modern techniques. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robus…
Beihang University（北京航空航天大学）；University College London（伦敦大学学院）；Fudan University（复旦大学）；Microsoft Research（微软研究院）
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P^2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P^2 Bot incorporates mutual persona perception to enha…
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
Leibniz University Hannover, Institute of Artificial Intelligence（汉诺威莱布尼茨大学人工智能研究所）；LMU Munich, MCML（慕尼黑大学MCML）；Paderborn University（帕德博恩大学）；Bielefeld University, CITEC（比勒菲尔德大学CITEC）
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）；Tencent Youtu Lab（腾讯优图实验室）
Michigan State University（密歇根州立大学）；TAL Education Group（好未来教育集团）
Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation data are biased. In particular, they can produce responses that reflect people's gender prejudice. Many debiasing methods have been developed for various NLP tasks, such as word embedding. However, they are not directly applicable to dialogue systems because they are likely to force dialogue models to generate similar responses for different genders. This greatly degrades the diversity of the generated responses and im…
Linguistic markers of personality traits have been studied extensively, but few cross-cultural studies exist. In this paper, we evaluate how native speakers of American English and Arabic perceive personality traits and naturalness of English utterances that vary along the dimensions of verbosity, hedging, lexical and syntactic alignment, and formality. The utterances are the turns within dialogue fragments that are presented as text transcripts to the workers of Amazon's Mechanical Turk. The results of the study suggest that all four dimensions can be used as linguistic markers of all persona…
In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribu…
ParaBridge: Bridging Paralinguistic Perception and Dialogue Behavior in Speech Language Models
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Tencent Hunyuan（腾讯混元）；Shenzhen Loop Area Institute（深圳河套学院）；Amphion Technology Co., Ltd.（Amphion科技有限公司）；Tsinghua University（清华大学）
Effectively recognising and applying emotions to interactions is a highly desirable trait for social robots. Implicitly understanding how subjects experience different kinds of actions and objects in the world is crucial for natural HRI interactions, with the possibility to perform positive actions and avoid negative actions. In this paper, we utilize the NICO robot's appearance and capabilities to give the NICO the ability to model a coherent affective association between a perceived auditory stimulus and a temporally asynchronous emotion expression. This is done by combining evaluations of e…
Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a larg…
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Digital Fujian Institute of Financial Big Data（数字福建金融大数据研究院）；Leiden University（莱顿大学）；College of Computer Science and Technology, Chongqing University of Technology（重庆理工大学计算机科学与技术学院）
CTSM: Combining Trait and State Emotions for Empathetic Response Model
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Digital Fujian Institute of Financial Big Data（数字福建金融大数据研究院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in th…
