Search Results for author:
Found 17 papers, 3 papers with code
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization
In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.
Ranked #11 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
dialog state tracking
Multi-domain Dialogue State Tracking
+2
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system.
Out-of-Task Training for Dialog State Tracking Models
Dialog state tracking (DST) suffers from severe data sparsity.
Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems
TUS can compete with rule-based user simulators on pre-defined domains and is able to generalise to unseen domains in a zero-shot fashion.
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance
This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.
Ranked #1 on
Emotion Recognition in Conversation
on EmoWoz
Emotion Recognition in Conversation
Task-Oriented Dialogue Systems
What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation
The dialogue management component of a task-oriented dialogue system is typically optimised via reinforcement learning (RL).
Robust Dialogue State Tracking with Weak Supervision and Sparse Data
Our architecture and training strategies improve robustness towards sample sparsity, new concepts and topics, leading to state-of-the-art performance on a range of benchmarks.
Dynamic Dialogue Policy for Continual Reinforcement Learning
The lack of a framework with training protocols, baseline models and suitable metrics, has so far hindered research in this direction.
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers
In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators.
Dialogue Evaluation with Offline Reinforcement Learning
They are ideally evaluated with human users, which however is unattainable to do at every iteration of the development phase.
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?
Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas.
EmoUS: Simulating User Emotions in Task-Oriented Dialogues
Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions.
From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue
Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs.
CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation
Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks.
