Search Results for author: Kam-Fai Wong

Found 48 papers, 8 papers with code

A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition

no code implementations EMNLP 2021 Huimin Wang, Kam-Fai Wong

Most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name.

Multi-agent Reinforcement Learning reinforcement-learning

Integrating Pretrained Language Model for Dialogue Policy Learning

no code implementations2 Nov 2021 Hongru Wang, Huimin Wang, Zezhong Wang, Kam-Fai Wong

Reinforcement Learning (RL) has been witnessed its potential for training a dialogue policy agent towards maximizing the accumulated rewards given from users.

Language Modelling

Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning

no code implementations13 Oct 2021 Hongru Wang, Zhijing Jin, Jiarun Cao, Gabriel Pui Cheong Fung, Kam-Fai Wong

However, previous works rarely investigate the effects of a different number of classes (i. e., $N$-way) and number of labeled data per class (i. e., $K$-shot) during training vs. testing.

Contrastive Learning Few-Shot Relation Classification

TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System

no code implementations11 Sep 2021 Hongru Wang, Mingyu Cui, Zimo Zhou, Gabriel Pui Cheong Fung, Kam-Fai Wong

A multi-turn dialogue always follows a specific topic thread, and topic shift at the discourse level occurs naturally as the conversation progresses, necessitating the model's ability to capture different topics and generate topic-aware responses.

Response Generation

Neural News Recommendation with Collaborative News Encoding and Structural User Encoding

1 code implementation Findings (EMNLP) 2021 Zhiming Mao, Xingshan Zeng, Kam-Fai Wong

In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning.

News Recommendation Reading Comprehension +1

MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging

no code implementations26 Aug 2021 Hongru Wang, Zezhong Wang, Gabriel Pui Cheong Fung, Kam-Fai Wong

Specifically, we propose a learn-from-memory mechanism that use explicit memory to keep track of the label representations of previously trained episodes and propose a contrastive learning method to compare the current label embedded in the few shot episode with the historic ones stored in the memory, and an adaption-from memory mechanism to determine the output label based on the contrast between the input labels embedded in the test episode and the label clusters in the memory.

Contrastive Learning Few-Shot Learning

Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations

1 code implementation ACL 2021 Lingzhi Wang, Xingshan Zeng, Kam-Fai Wong

To help individuals express themselves better, quotation recommendation is receiving growing attention.

KddRES: A Multi-level Knowledge-driven Dialogue Dataset for Restaurant Towards Customized Dialogue System

no code implementations17 Nov 2020 Hongru Wang, Min Li, Zimo Zhou, Gabriel Pui Cheong Fung, Kam-Fai Wong

In this paper, we publish a first Cantonese knowledge-driven Dialogue Dataset for REStaurant (KddRES) in Hong Kong, which grounds the information in multi-turn conversations to one specific restaurant.

Learning Efficient Dialogue Policy from Demonstrations through Shaping

no code implementations ACL 2020 Huimin Wang, Baolin Peng, Kam-Fai Wong

Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users.

Domain Adaptation reinforcement-learning

Dynamic Online Conversation Recommendation

no code implementations ACL 2020 Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.

Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition

no code implementations NAACL 2021 Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong

We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for classification and sequence labelling) to jointly extract dialogue states.

Classification Dialogue State Tracking +3

Neural Conversation Recommendation with Online Interaction Modeling

no code implementations IJCNLP 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.

Collaborative Filtering

Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks

no code implementations ACL 2019 Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications.

Joint Effects of Context and User History for Predicting Online Conversation Re-entries

1 code implementation ACL 2019 Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong

We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.

A Joint Model of Conversational Discourse Latent Topics on Microblogs

no code implementations CL 2018 Jing Li, Yan Song, Zhongyu Wei, Kam-Fai Wong

To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information.

Topic Models

A Joint Model of Conversational Discourse and Latent Topics on Microblogs

no code implementations11 Sep 2018 Jing Li, Yan Song, Zhongyu Wei, Kam-Fai Wong

To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information.

Topic Models

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

no code implementations NAACL 2018 Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong

We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics.

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

3 code implementations ACL 2018 Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su

During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience.

Task-Completion Dialogue Policy Learning

IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases

no code implementations IJCNLP 2017 Liang-Chih Yu, Lung-Hao Lee, Jin Wang, Kam-Fai Wong

This paper presents the IJCNLP 2017 shared task on Dimensional Sentiment Analysis for Chinese Phrases (DSAP) which seeks to identify a real-value sentiment score of Chinese single words and multi-word phrases in the both valence and arousal dimensions.

Sentiment Analysis

NLPTEA 2017 Shared Task -- Chinese Spelling Check

no code implementations WS 2017 Gabriel Fung, Maxime Debosschere, Dingmin Wang, Bo Li, Jia Zhu, Kam-Fai Wong

This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017.

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

no code implementations31 Oct 2017 Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems.

Task-Completion Dialogue Policy Learning

An Attentional Neural Conversation Model with Improved Specificity

no code implementations3 Jun 2016 Kaisheng Yao, Baolin Peng, Geoffrey Zweig, Kam-Fai Wong

Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.

Towards Neural Network-based Reasoning

1 code implementation22 Aug 2015 Baolin Peng, Zhengdong Lu, Hang Li, Kam-Fai Wong

For example, it improves the accuracy on Path Finding(10K) from 33. 4% [6] to over 98%.

Quantising Opinions for Political Tweets Analysis

no code implementations LREC 2012 Yulan He, Hassan Saif, Zhongyu Wei, Kam-Fai Wong

There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues.

Sentiment Analysis

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