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arXiv 2012.08377cs.CLcs.AI
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU)（阿里巴巴-南洋理工大学新加坡联合研究院，南洋理工大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU（南洋理工大学-不列颠哥伦比亚大学老年人积极生活联合卓越研究中心，南洋理工大学）；School of Electrical and Electronic Engineering, NTU（南洋理工大学电气与电子工程学院）；Alibaba Group（阿里巴巴集团）
Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Tencent AI Lab（腾讯人工智能实验室）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional con…
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Department of Electrical Engineering and Computer Science, Indian Institute of Science Education and Research Bhopal（电子工程与计算机科学系，比哈尔印度科学教育与研究中心）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information. Towards the issue of introducing related knowledge, we propose a semantic-driven knowledge-aware QA framework, which controls the knowledge injection in a coarse-to-careful fashion. We devise a tailoring strategy to filter extracted knowledge under monitoring of the coarse semantic of question on the knowledge extraction stage. And we develop a semantic-aware knowledge fetching module that…
Fudan University（复旦大学）；Fudan-Aishu Cognitive Intelligence Joint Research Center（复旦-爱数认知智能联合研究中心）
Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language m…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Improving the emotional awareness of pre-trained language models is an emerging important problem for dialogue generation tasks. Although prior studies have introduced methods to improve empathetic dialogue generation, few have discussed how to incorporate commonsense knowledge into pre-trained language models for controllable dialogue generation. In this study, we propose a novel framework that improves empathetic dialogue generation using pre-trained language models by 1) incorporating commonsense knowledge through prompt verbalization, and 2) controlling dialogue generation using a strategy…
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；WeChat AI, Tencent Inc（腾讯微信人工智能）；Meituan Group（美团集团）
Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing w…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Inc（腾讯公司）
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs to overcome implicit contextual modelling of the original entailment style. Following the previous work, we further introduce the emotion information into graphs. Emotion information can markedly promote the detection of causal utterances whose emotion is the same as the targeted utterance. However, it is still hard to detect causal utterances with different emotions, especially neutral ones. The reason is that models…
Google Assistant（谷歌助理）；University of Washington（华盛顿大学）
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored. In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL. We formulate TIME-DIAL as a multiple-choice cloze task with …
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
Warsaw University of Technology（华沙理工大学）；Wrocław University of Science and Technology（弗罗茨瓦夫理工大学）；University of Warsaw（华沙大学）；Lincoln University College（林肯大学学院）
In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providin…
CARE: Counselor-Aligned Response Engine for Online Mental-Health Support
Ben-Gurion University（本· Gurion 大学）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；School of Informatics, University of Edinburgh Edinburgh UK（爱丁堡大学信息学院爱丁堡 英国）
The Effect of Emotional Context on Large Language Models' Endorsement of Premature Decisions: Comparing Emotional Vulnerability Across Six Commercial Models
Yonsei University（延世大学）；Fudan University（复旦大学）；St. Johnsbury Academy Jeju（济州圣约翰斯伯里学院）
School of Computer and Communication Sciences（计算机与通信科学学院）；École Polytechnique Fédérale de Lausanne, Switzerland（洛桑联邦理工学院）
Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we desc…
Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety
Faculty of Computer Science, University of New Brunswick（新不伦瑞克大学计算机科学学院）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
