Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)最新文献

A Token-pair Framework for Information Extraction from Dialog Transcripts in SereTOD Challenge 从对话文本中提取信息的令牌对框架

Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.seretod-1.3

Chenyue Wang, Xiangxing Kong, Mengzuo Huang, Feng Li, Jian Xing, Weidong Zhang, Wuhe Zou

引用次数: 0

CMCC: A Comprehensive and Large-Scale Human-Human Dataset for Dialogue Systems CMCC:一个面向对话系统的全面大规模人-人数据集

Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.seretod-1.7

Y. Huang, Xiaoting Wu, Si Chen, Wei Hu, Qing Zhu, Junlan Feng, Chao Deng, Zhijian Ou, Jiangjiang Zhao

{"title":"CMCC: A Comprehensive and Large-Scale Human-Human Dataset for Dialogue Systems","authors":"Y. Huang, Xiaoting Wu, Si Chen, Wei Hu, Qing Zhu, Junlan Feng, Chao Deng, Zhijian Ou, Jiangjiang Zhao","doi":"10.18653/v1/2022.seretod-1.7","DOIUrl":"https://doi.org/10.18653/v1/2022.seretod-1.7","url":null,"abstract":"Dialogue modeling problems severely limit the real-world deployment of neural conversational models and building a human-like dialogue agent is an extremely challenging task. Recently, data-driven models become more and more prevalent which need a huge amount of conversation data. In this paper, we release around 100,000 dialogue, which come from real-world dialogue transcripts between real users and customer-service staffs. We call this dataset as CMCC (China Mobile Customer Care) dataset, which differs from existing dialogue datasets in both size and nature significantly. The dataset reflects several characteristics of human-human conversations, e.g., task-driven, care-oriented, and long-term dependency among the context. It also covers various dialogue types including task-oriented, chitchat and conversational recommendation in real-world scenarios. To our knowledge, CMCC is the largest real human-human spoken dialogue dataset and has dozens of times the data scale of others, which shall significantly promote the training and evaluation of dialogue modeling methods. The results of extensive experiments indicate that CMCC is challenging and needs further effort. We hope that this resource will allow for more effective models across various dialogue sub-problems to be built in the future.","PeriodicalId":171614,"journal":{"name":"Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A GlobalPointer based Robust Approach for Information Extraction from Dialog Transcripts 基于全局指针的对话文本信息提取方法

Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.seretod-1.2

Yanbo J. Wang, Sheng Chen, Hengxing Cai, Wei Wei, Kuo Yan, Zhe Sun, Hui Qin, Yuming Li, Xiaocheng Cai

引用次数: 0

State-Aware Adversarial Training for Utterance-Level Dialogue Generation 话语级对话生成的状态意识对抗性训练

Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.seretod-1.8

Y. Huang, Xiaoting Wu, Wei Hu, Junlan Feng, Chao Deng

引用次数: 0

Offline-to-Online Co-Evolutional User Simulator and Dialogue System 离线到在线协同进化用户模拟器和对话系统

Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.seretod-1.11

Dafeng Chi, Yuzheng Zhuang, Yao Mu, Bin Wang, Jianzhu Bao, Yasheng Wang, Yuhan Dong, Xin Jiang, Qun Liu, Jianye Hao

{"title":"Offline-to-Online Co-Evolutional User Simulator and Dialogue System","authors":"Dafeng Chi, Yuzheng Zhuang, Yao Mu, Bin Wang, Jianzhu Bao, Yasheng Wang, Yuhan Dong, Xin Jiang, Qun Liu, Jianye Hao","doi":"10.18653/v1/2022.seretod-1.11","DOIUrl":"https://doi.org/10.18653/v1/2022.seretod-1.11","url":null,"abstract":"Reinforcement learning (RL) has emerged as a promising approach to fine-tune offline pretrained GPT-2 model in task-oriented dialogue (TOD) systems. In order to obtain human-like online interactions while extending the usage of RL, building pretrained user simulators (US) along with dialogue systems (DS) and facilitating jointly fine-tuning via RL becomes prevalent. However, joint training brings distributional shift problem caused by compounding exposure bias. Existing methods usually iterative update US and DS to ameliorate the ensued non-stationarity problem, which could lead to sub-optimal policy and less sample efficiency. To take a step further for tackling the problem, we introduce an Offline-to-oNline Co-Evolutional (ONCE) framework, which enables bias-aware concurrent joint update for RL-based fine-tuning whilst takes advantages from GPT-2 based end-to-end modeling on US and DS. Extensive experiments demonstrate that ONCE builds high-quality loops of policy learning and dialogues data collection, and achieves state-of-the-art online and offline evaluation results on MultiWOZ2.1 dataset. Opensourced code will be implemented with Mindspore (MS, 2022) and released on our homepage.","PeriodicalId":171614,"journal":{"name":"Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115302061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0