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Found 57 papers, 7 papers with code
Similarity Scoring for Dialogue Behaviour Comparison
The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models.
Blending Task Success and User Satisfaction: Analysis of Learned Dialogue Behaviour with Multiple Rewards
Recently, principal reward components for dialogue policy reinforcement learning use task success and user satisfaction independently and neither the resulting learned behaviour has been analysed nor a suitable proper analysis method even existed.
From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems
Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations.
Towards Modelling Self-imposed Filter Bubbles in Argumentative Dialogue Systems
To build a well-founded opinion it is natural for humans to gather and exchange new arguments.
Towards Building a Spoken Dialogue System for Argument Exploration
We evaluate the likeability and motivation of users to interact with the new system in a user study.
User Interest Modelling in Argumentative Dialogue Systems
Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest.
A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction
After this conversion, the text representation of the dialogue state graph is included as part of the prompt of a large language model used to decode the agent response.
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring
Constructing responses in task-oriented dialogue systems typically relies on information sources such the current dialogue state or external databases.
Fostering User Engagement in the Critical Reflection of Arguments
To support a fair and unbiased opinion-building process, we propose a chatbot system that engages in a deliberative dialogue with a human.
System-Initiated Transitions from Chit-Chat to Task-Oriented Dialogues with Transition Info Extractor and Transition Sentence Generator
In this work, we study dialogue scenarios that start from chit-chat but eventually switch to task-related services, and investigate how a unified dialogue model, which can engage in both chit-chat and task-oriented dialogues, takes the initiative during the dialogue mode transition from chit-chat to task-oriented in a coherent and cooperative manner.
Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues
We contribute two efficient prompt models which can proactively generate a transition sentence to trigger system-initiated transitions in a unified dialogue model.
GraphWOZ: Dialogue Management with Conversational Knowledge Graphs
We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state.
ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation
The pre-trained conversational models still fail to capture the implicit commonsense (CS) knowledge hidden in the dialogue interaction, even though they were pre-trained with an enormous dataset.
Context Matters in Semantically Controlled Language Generation for Task-oriented Dialogue Systems
We utilize the pre-trained multi-context ConveRT model for context representation in a model trained from scratch; and leverage the immediate preceding user utterance for context generation in a model adapted from the pre-trained GPT-2.
Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge
One challenge for dialogue agents is to recognize feelings of the conversation partner and respond accordingly.
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT
This paper presents an automatic method to evaluate the naturalness of natural language generation in dialogue systems.
Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain
Intent classifier model stacks BiLSTM with attention mechanism on top of the pre-trained BERT model and fine-tune the model for recognizing the user intent, whereas the argument similarity model employs BERT+BiLSTM for identifying system arguments the user refers to in his or her natural language utterances.
Estimating User Communication Styles for Spoken Dialogue Systems
We use this corpus to estimate the elaborateness and the directness of each utterance.
Comparative Study of Sentence Embeddings for Contextual Paraphrasing
Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented.
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems
We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments.
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years.
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.
Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks
In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance.
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.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
Dialogue assistants are rapidly becoming an indispensable daily aid.
Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems
Defining all possible system actions in a dialogue system by hand is a tedious work.
Cultural Vocal Bursts Intensity Prediction
Spoken Dialogue Systems
DialPort, Gone Live: An Update After A Year of Development
DialPort collects user data for connected spoken dialog systems.
Interaction Quality Estimation Using Long Short-Term Memories
For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance.
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e. g., the dialogue success and the dialogue length.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
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.
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.
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)
+6
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.
First Insight into Quality-Adaptive Dialogue
While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone.
Comparison of Gender- and Speaker-adaptive Emotion Recognition
In this contribution, we argue that adding information unique for each speaker, i. e., by using speaker identification techniques, improves emotion recognition simply by adding this additional information to the feature vector of the statistical classification algorithm.
A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let's Go Bus Information System
Standardized corpora are the foundation for spoken language research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
