33 citations · 189 across the 14 of their papers we have counts for
14 papers
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Peng Jin, Ryuichi Takanobu, Wancai Zhang +2
Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversati…
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…
MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation
Ting Han, Ximing Liu, Ryuichi Takanobu +5
Task-oriented dialogue systems have made unprecedented progress with multiple state-of-the-art (SOTA) models underpinned by a number of publicly available MultiWOZ datasets. Dialog…
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…
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
Qi Zhu, Zheng Zhang, Yan Fang +7
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and di…
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Ryuichi Takanobu, Hanlin Zhu, Minlie Huang
Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learni…
Deep Conversational Recommender in Travel
Lizi Liao, Ryuichi Takanobu, Yunshan Ma +3
When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries. However,…
ConvLab: Multi-Domain End-to-End Dialog System Platform
Sungjin Lee, Qi Zhu, Ryuichi Takanobu +8
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a lar…
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
Ryuichi Takanobu, Tao Zhuang, Minlie Huang +3
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that m…
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu +1
Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modele…