Papers User Simulation
“User Simulation” 태그가 달린 논문 64편 · 필터 해제
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing t…
Conversational RecommendationRecommendation SystemsUser SimulationState of the Art of User Simulation approaches for conversational information retrieval
Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs. In order to optimize these inter…
Decision MakingInformation Retrievalreinforcement-learningReinforcement Learning+4Reactive and Safe Road User Simulations using Neural Barrier Certificates
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the origi…
Imitation LearningUser SimulationDomain-independent User Simulation with Transformers for Task-oriented Dialogue Systems
Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive. Many set-ups therefore rely on a user simulator…
Task-Oriented Dialogue SystemsUser SimulationSimulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
Evaluation is crucial in the development process of task-oriented dialogue systems. As an evaluation method, user simulation allows us to tackle issues such as scalability and cost-efficiency, making it a viable choice f…
Domain GeneralizationMovie RecommendationTask-Oriented Dialogue SystemsUser SimulationAdvances and Challenges in Conversational Recommender Systems: A Survey
Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important …
Conversational RecommendationDialogue UnderstandingInformation RetrievalRecommendation Systems+3Evaluating Conversational Recommender Systems via User Simulation
Conversational information access is an emerging research area. Currently, human evaluation is used for end-to-end system evaluation, which is both very time and resource intensive at scale, and thus becomes a bottleneck…
Conversational Information AccessRecommendation SystemsUser SimulationEnergy-efficient Deployment of Multiple UAVs Using Ellipse Clustering to Establish Base Stations
The demand for future wireless communication systems is being satisfied for various circumstances through unmanned aerial vehicles (UAVs), which act as flying base stations (BSs). In this letter, we propose an ellipse cl…
ClusteringUser SimulationCrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dat…
Dialogue State TrackingTask-Oriented Dialogue SystemsUser SimulationVariational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the ta…
Data Augmentationdialog state trackingDialogue State TrackingResponse Generation+2How to Build User Simulators to Train RL-based Dialog Systems
User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a good user simulator that models real user b…
Reinforcement LearningReinforcement Learning (RL)User SimulationUser Modeling for Task Oriented Dialogues
We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of diff…
Dialogue State TrackingDiversityTask-Oriented Dialogue SystemsUser SimulationNeural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems
User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and i…
Dialogue ManagementDiversityReinforcement LearningSpoken Dialogue Systems+2Explainable Agreement through Simulation for Tasks with Subjective Labels
The field of information retrieval often works with limited and noisy data in an attempt to classify documents into subjective categories, e.g., relevance, sentiment and controversy. We typically quantify a notion of agr…
Information RetrievalRetrievalUser SimulationNeural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems
User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and i…
DiversityReinforcement LearningSpoken Dialogue SystemsTask-Oriented Dialogue Systems+1Massive MIMO relaying with linear precoding in correlated channels under limited feedback
In this paper we study on a massive MIMO relay system with linear precoding under the conditions of imperfect channel state information at the transmitter (CSIT) and per-user channel transmit correlation. In our system t…
User SimulationA User Simulator for Task-Completion Dialogues
Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction wi…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Task-Oriented Dialogue Systems+1A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems
User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history int…
DecoderDialogue State TrackingSpoken Dialogue SystemsUser SimulationContinuously Learning Neural Dialogue Management
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue d…
Dialogue ManagementManagementreinforcement-learningReinforcement Learning+3Optimizing human-interpretable dialog management policy using Genetic Algorithm
Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be …
Managementreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1