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Found 10 papers, 4 papers with code
AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model
As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists.
Database Search Results Disambiguation for Task-Oriented Dialog Systems
We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.
Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics
Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance.
Ranked #4 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
Robust Conversational AI with Grounded Text Generation
This article provides an overview of this progress and discusses related methods and technologies that can be incorporated for building robust conversational AI systems.
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale.
Ranked #4 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems
Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows.
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
