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Found 19 papers, 12 papers with code
Turn-Level User Satisfaction Estimation in E-commerce Customer Service
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.
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations.
Image-based Generative Performance Benchmarking
Language Modelling
+9
Manual-Guided Dialogue for Flexible Conversational Agents
Modeling these subtasks is consistent with the human agent's behavior patterns.
End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management
To address this task, we propose a TOD system with semi-structured knowledge management, SeKnow, which extends the belief state to manage knowledge with both structured and unstructured contents.
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
To address this task, we propose a TOD system with hybrid knowledge management, HyKnow.
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.
Robustness Testing of Language Understanding in Task-Oriented Dialog
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.
Joint Event Extraction with Hierarchical Policy Network
Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation.
Ranked #5 on
Recommendation Systems
on ReDial
MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation
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.
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
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
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
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.
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
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
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
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.
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
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.
Ranked #2 on
Relation Extraction
on NYT24
Entity Extraction using GAN
Hierarchical Reinforcement Learning
+4
