TFSM-based dialogue management model framework for affective dialogue systems

2015 ◽  
Vol 10 (4) ◽  
pp. 404-410 ◽  
Author(s):  
Fuji Ren ◽  
Yu Wang ◽  
Changqin Quan
Keyword(s):  
Management Model ◽  
Dialogue Systems ◽  
Model Framework ◽  
Dialogue Management

scholarly journals Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

2019 ◽  
Vol 37 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Zheng Zhang ◽  
Minlie Huang ◽  
Zhongzhou Zhao ◽  
Feng Ji ◽  
Haiqing Chen ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Dialogue Management ◽  
Task Oriented

Dialogue management for leading the conversation in persuasive dialogue systems

2013 ◽  
Author(s):  
Takuya Hiraoka ◽  
Yuki Yamauchi ◽  
Graham Neubig ◽  
Sakriani Sakti ◽  
Tomoki Toda ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Dialogue Management

scholarly journals Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management

2021 ◽  
Vol 9 ◽  
pp. 36-52
Author(s):  
Milan Gritta ◽  
Gerasimos Lampouras ◽  
Ignacio Iacobacci
Keyword(s):  
Data Augmentation ◽  
Training Data ◽  
Dialogue Systems ◽  
Success Rates ◽  
Agent Behavior ◽  
Graph Data ◽  
Dialogue Management ◽  
Data Volume ◽  
Task Oriented ◽  
Training Signal

Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size con- sidering the complexity of the dialogues. Additionally, conventional training signal in- ference is not suitable for non-deterministic agent behavior, namely, considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non- deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.

scholarly journals TODO: A Core Ontology for Task-Oriented Dialogue Systems in Industry 4.0

2021 ◽  
Author(s):  
Cristina Aceta ◽  
Izaskun Fernández ◽  
Aitor Soroa
Keyword(s):  
Industry 4.0 ◽  
Use Cases ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Industrial Systems ◽  
Dialogue Management ◽  
Core Ontology ◽  
Dialogue Modelling ◽  
The One ◽  
Task Oriented

Nowadays, the demand in industry of dialogue systems to be able to naturally communicate with industrial systems is increasing, as they allow to enhance productivity and security in these scenarios. However, adapting these systems to different use cases is a costly process, due to the complexity of the scenarios and the lack of available data. This work presents the Task-Oriented Dialogue management Ontology (TODO), which aims to provide a core and complete base for semantic-based task-oriented dialogue systems in the context of industrial scenarios in terms of, on the one hand, domain and dialogue modelling and, on the other hand, dialogue management and tracing support. Furthermore, its modular structure, besides grouping specific knowledge in independent components, allows to easily extend each of the modules, attending the necessities of the different use cases. These characteristics allow an easy adaptation of the ontology to different use cases, with a considerable reduction of time and costs. So as to demonstrate the capabilities of the the ontology by integrating it in a task-oriented dialogue system, TODO has been validated in real-world use cases. Finally, an evaluation is also presented, covering different relevant aspects of the ontology.

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