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Found 48 papers, 8 papers with code
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition
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.
A Survey on Recent Advances and Challenges in Reinforcement LearningMethods for Task-Oriented Dialogue Policy Learning
In this paper, we survey recent advances and challenges in dialogue policy from the prescriptive of RL.
Integrating Pretrained Language Model for Dialogue Policy Learning
Reinforcement Learning (RL) has been witnessed its potential for training a dialogue policy agent towards maximizing the accumulated rewards given from users.
Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph
In this paper, we present a pre-trained language model (PLM) based framework called RID for conversational recommender system (CRS).
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning
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.
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning
To tackle this problem, we propose an effective similarity-based method to select data from the source domains.
TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System
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.
Re-entry Prediction for Online Conversations via Self-Supervised Learning
In recent years, world business in online discussions and opinion sharing on social media is booming.
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
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.
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging
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.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations
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
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
Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users.
Dynamic Online Conversation Recommendation
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.
CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and Prediction with Multi-task Learning
This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks.
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
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.
Neural Conversation Recommendation with Online Interaction Modeling
The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.
Coupling Global and Local Context for Unsupervised Aspect Extraction
Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
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
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
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.
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
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.
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
Automatic rumor detection is technically very challenging.
Task-oriented Dialogue System for Automatic Diagnosis
In this paper, we make a move to build a dialogue system for automatic diagnosis.
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse
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
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.
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
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.
NLPTEA 2017 Shared Task -- Chinese Spelling Check
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
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.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
How fake news goes viral via social media?
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks.
reinforcement-learning
Task-Completion Dialogue Policy Learning
May I take your order? A Neural Model for Extracting Structured Information from Conversations
This is motivated by an actual system under development to assist in the order taking process.
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese.
An Attentional Neural Conversation Model with Improved Specificity
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
For example, it improves the accuracy on Path Finding(10K) from 33. 4% [6] to over 98%.
The CUHK Discourse TreeBank for Chinese: Annotating Explicit Discourse Connectives for the Chinese TreeBank
The lack of open discourse corpus for Chinese brings limitations for many natural language processing tasks.
Quantising Opinions for Political Tweets Analysis
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.
