33 citations · 74 across the 5 of their papers we have counts for
11 papers
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
Silin Gao, Ryuichi Takanobu, Wei Peng +2
Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g. ontologies and databases) to guide the goal-oriented conversations. However, they fall short of handl…
Robustness Testing of Language Understanding in Task-Oriented Dialog
Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen +6
Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution…
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Yujia Qin, Yankai Lin, Ryuichi Takanobu +6
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do n…
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
Ryuichi Takanobu, Qi Zhu, Jinchao Li +3
There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations. Although many methods are…
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
Ryuichi Takanobu, Runze Liang, Minlie Huang
Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large nu…
Recent Advances and Challenges in Task-oriented Dialog System
Zheng Zhang, Ryuichi Takanobu, Qi Zhu +2
Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academi…