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Found 17 papers, 9 papers with code
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks.
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning
Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions.
Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
To address these limitations, we introduce Zero-shot Interactive Personalized Object Navigation (ZIPON), where robots need to navigate to personalized goal objects while engaging in conversations with users.
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language.
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation
Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data.
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
Task-Oriented Dialogue System as Natural Language Generation
In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Transferable Dialogue Systems and User Simulators
Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Existing end-to-end dialog systems perform less effectively when data is scarce.
A Survey on Dialog Management: Recent Advances and Challenges
Dialog management (DM) is a crucial component in a task-oriented dialog system.
Elastic CRFs for Open-ontology Slot Filling
An ontology is defined by the collection of slots and the values that each slot can take.
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis.
Tracking of enriched dialog states for flexible conversational information access
The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible accessing movie information.
