Search Results for author: Ryuichi Takanobu

Found 17 papers, 10 papers with code

Turn-Level User Satisfaction Estimation in E-commerce Customer Service

no code implementations ACL (ECNLP) 2021 Runze Liang, Ryuichi Takanobu, Feng-Lin Li, Ji Zhang, Haiqing Chen, Minlie Huang

To this end, we formalize the turn-level satisfaction estimation as a reinforcement learning problem, in which the model can be optimized with only session-level satisfaction labels.

End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management

no code implementations22 Jun 2021 Silin Gao, Ryuichi Takanobu, Minlie Huang

In this paper, we formulate a task of modeling TOD grounded on a fusion of structured and unstructured knowledge.

Language Modelling

Robustness Testing of Language Understanding in Task-Oriented Dialog

2 code implementations ACL 2021 Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang

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.

Data Augmentation Language understanding +1

Joint Event Extraction with Hierarchical Policy Network

no code implementations COLING 2020 Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, Weidong Xiao

Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.

Event Detection Event Extraction +1

CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation

1 code implementation EMNLP 2021 Wenchang Ma, Ryuichi Takanobu, Minlie Huang

Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation.

Recommendation Systems Text Generation

MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation

4 code implementations12 Oct 2020 Ting Han, Ximing Liu, Ryuichi Takanobu, Yixin Lian, Chongxuan Huang, Dazhen Wan, Wei Peng, Minlie Huang

In this paper, we introduce MultiWOZ 2. 3, in which we differentiate incorrect annotations in dialogue acts from dialogue states, identifying a lack of co-reference when publishing the updated dataset.

Dialogue State Tracking Language understanding +2

Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation

no code implementations SIGDIAL (ACL) 2020 Ryuichi Takanobu, Qi Zhu, Jinchao Li, Baolin Peng, Jianfeng Gao, Minlie Huang

There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.

Goal-Oriented Dialog

Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition

1 code implementation ACL 2020 Ryuichi Takanobu, Runze Liang, Minlie Huang

To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents.

Recent Advances and Challenges in Task-oriented Dialog System

no code implementations17 Mar 2020 Zheng Zhang, Ryuichi Takanobu, Qi Zhu, Minlie Huang, Xiaoyan Zhu

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 academic and industrial communities.

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

1 code implementation ACL 2020 Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

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 diagnose the weakness of systems.

Task-Oriented Dialogue Systems

Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog

1 code implementation IJCNLP 2019 Ryuichi Takanobu, Hanlin Zhu, Minlie Huang

Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals.

Deep Conversational Recommender in Travel

no code implementations25 Jun 2019 Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

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.

ConvLab: Multi-Domain End-to-End Dialog System Platform

1 code implementation ACL 2019 Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Xiang Li, Yaoqin Zhang, Zheng Zhang, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

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 large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

2 code implementations9 Nov 2018 Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, Minlie Huang

The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.

Entity Extraction using GAN Hierarchical Reinforcement Learning +1

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