88 citations · 245 across the 32 of their papers we have counts for
35 papers
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Kuan Zhang, Yukun Chen, Zhihao Yang +8
Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these…
Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability
Kuan Zhang, Yukun Chen, Zhihao Yang +7
Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent und…
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
Minzheng Wang, Run Luo, Yanbo Wang +6
While Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for closed-ended tasks, extending it to open-ended social language games via self-play reveals a cr…
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Taha Aksu, Nancy F. Chen
Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the…
PIPPA: A Partially Synthetic Conversational Dataset
Tear Gosling, Alpin Dale, Yinhe Zheng
With the emergence of increasingly powerful large language models, there is a burgeoning interest in leveraging these models for casual conversation and role-play applications. How…
Long-Tailed Question Answering in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +2
Real-world data often have an open long-tailed distribution, and building a unified QA model supporting various tasks is vital for practical QA applications. However, it is non-tri…
Domain Incremental Lifelong Learning in an Open World
Yi Dai, Hao Lang, Yinhe Zheng +3
Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously. Architecture-based approaches are reported to be effective implementations for LL mod…
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Hao Lang, Yinhe Zheng, Binyuan Hui +2
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent de…
A Survey on Out-of-Distribution Detection in NLP
Hao Lang, Yinhe Zheng, Yixuan Li +3
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past year…
Empathetic Response Generation via Emotion Cause Transition Graph
Yushan Qian, Bo Wang, Ting-En Lin +6
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). Besid…
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian +6
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems. To address the catastrophic forgetting issue of LL, generative replay methods are widely employed…
Semi-Supervised Lifelong Language Learning
Yingxiu Zhao, Yinhe Zheng, Bowen Yu +6
Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when learning tasks sequentially. However, existing lifelong language learning methods only foc…
Estimating Soft Labels for Out-of-Domain Intent Detection
Hao Lang, Yinhe Zheng, Jian Sun +3
Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD s…
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Chujie Zheng, Jinfeng Zhou, Yinhe Zheng +6
Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging.…
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
Sahand Sabour, Wen Zhang, Xiyao Xiao +5
The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availab…
Continuous QA Learning with Structured Prompts
Yinhe Zheng
QA models with lifelong learning (LL) abilities are important for practical QA applications, and architecture-based LL methods are reported to be an effective implementation for th…
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)
Yinhe Zheng, Guanyi Chen
We have noticed that Marek et al. (2021) try to re-implement our paper Zheng et al. (2020a) in their work "OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation"…
Rethinking and Refining the Distinct Metric
Siyang Liu, Sahand Sabour, Yinhe Zheng +3
Distinct- score\cite{Li2016} is a widely used automatic metric for evaluating diversity in language generation tasks. However, we observed that the original approach for calcula…
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis
Yequan Wang, Xuying Meng, Yiyi Liu +4
Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e. to predict the emotion…
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao +5
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available. Optimization-based meta-learning algorithms ach…
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Wanwei He, Yinpei Dai, Yinhe Zheng +9
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and ge…
Unsupervised Domain Adaptation with Adapter
Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao +1
Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from vari…
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits
Chen Henry Wu, Yinhe Zheng, Xiaoxi Mao +1
Grounded dialogue models generate responses that are grounded on certain concepts. Limited by the distribution of grounded dialogue data, models trained on such data face the trans…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…