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Found 24 papers, 9 papers with code
GCDF1: A Goal- and Context- Driven F-Score for Evaluating User Models
The evaluation of dialogue systems in interaction with simulated users has been proposed to improve turn-level, corpus-based metrics which can only evaluate test cases encountered in a corpus and cannot measure system’s ability to sustain multi-turn interactions.
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata.
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
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.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems
In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS).
A Generative Model for Joint Natural Language Understanding and Generation
This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG.
Natural Language Understanding
Task-Oriented Dialogue Systems
+1
Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.
Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation
Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain.
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e. g., relations.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available. To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics. At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora. The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided.
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.
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.
Feudal Dialogue Management with Jointly Learned Feature Extractors
Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains.
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems
This paper presents two ways of dealing with scarce data in semantic decoding using N-Best speech recognition hypotheses.
Feudal Reinforcement Learning for Dialogue Management in Large Domains
Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user.
Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring
With the popularity of mobile devices, personalized speech recognizer becomes more realizable today and highly attractive.
