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Found 21 papers, 4 papers with code
Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges
This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog.
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).
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning
Experiments show that AgentGraph models significantly outperform traditional reinforcement learning approaches on most of the 18 tasks of the PyDial benchmark.
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.
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.
Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems
The ABUS is based on hand-crafted rules and its output is in semantic form.
DialPort, Gone Live: An Update After A Year of Development
DialPort collects user data for connected spoken dialog systems.
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented.
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Dialogue manager domain adaptation using Gaussian process reinforcement learning
Spoken dialogue systems allow humans to interact with machines using natural speech.
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks.
Continuously Learning Neural Dialogue Management
We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems.
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning.
A Network-based End-to-End Trainable Task-oriented Dialogue System
Teaching machines to accomplish tasks by conversing naturally with humans is challenging.
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems
Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains.
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Reward shaping is one promising technique for addressing these concerns.
Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems
The models are trained on dialogues generated by a simulated user and the best model is then used to train a policy on-line which is shown to perform at least as well as a baseline system using prior knowledge of the user's task.
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on.
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality.
