A Storytelling Robot Managing Persuasive and Ethical Stances via ACT-R: An Exploratory Study

We present a storytelling robot, controlled via the ACT-R cognitive architecture, able to adopt different persuasive techniques and ethical stances while conversing about some topics concerning COVID-19. The main contribution of the paper consists in the proposal of a needs-driven model that guides and evaluates, during the dialogue, the use (if any) of persuasive techniques available in the agent procedural memory. The portfolio of persuasive techniques tested in such a model ranges from the use of storytelling to framing techniques and rhetorical-based arguments. To the best of our knowledge, this represents the first attempt of building a persuasive agent able to integrate a mix of explicitly grounded cognitive assumptions about dialogue management, storytelling and persuasive techniques as well as ethical attitudes. The paper presents the results of an exploratory evaluation of the system on 63 participants.

A Unified Dialogue Management Strategy for Multi-intent Dialogue Conversations in Multiple Languages

Building Virtual Agents capable of carrying out complex queries of the user involving multiple intents of a domain is quite a challenge, because it demands that the agent manages several subtasks simultaneously. This article presents a universal Deep Reinforcement Learning framework that can synthesize dialogue managers capable of working in a task-oriented dialogue system encompassing various intents pertaining to a domain. The conversation between agent and user is broken down into hierarchies, to segregate subtasks pertinent to different intents. The concept of Hierarchical Reinforcement Learning, particularly options , is used to learn policies in different hierarchies that operates in distinct time steps to fulfill the user query successfully. The dialogue manager comprises top-level intent meta-policy to select among subtasks or options and a low-level controller policy to pick primitive actions to communicate with the user to complete the subtask provided to it by the top-level policy in varying intents of a domain. The proposed dialogue management module has been trained in a way such that it can be reused for any language for which it has been developed with little to no supervision. The developed system has been demonstrated for “Air Travel” and “Restaurant” domain in English and Hindi languages. Empirical results determine the robustness and efficacy of the learned dialogue policy as it outperforms several baselines and a state-of-the-art system.

Development of Dialogue Management System for Banking Services

Rapid increase in conversational AI and user chat data lead to intensive development of dialogue management systems (DMS) for various industries. Yet, for low-resource languages, such as Azerbaijani, very little research has been conducted. The main purpose of this work is to experiment with various DMS pipeline set-ups to decide on the most appropriate natural language understanding and dialogue manager settings. In our project, we designed and evaluated different DMS pipelines with respect to the conversational text data obtained from one of the leading retail banks in Azerbaijan. In the work, the main two components of DMS—Natural language Understanding (NLU) and Dialogue Manager—have been investigated. In the first step of NLU, we utilized a language identification (LI) component for language detection. We investigated both built-in LI methods such as fastText and custom machine learning (ML) models trained on the domain-based dataset. The second step of the work was a comparison of the classic ML classifiers (logistic regression, neural networks, and SVM) and Dual Intent and Entity Transformer (DIET) architecture for user intention detection. In these experiments we used different combinations of feature extractors such as CountVectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer, and word embeddings for both word and character n-gram based tokens. To extract important information from the text messages, Named Entity Extraction (NER) component was added to the pipeline. The best NER model was chosen among conditional random fields (CRF) tagger, deep neural networks (DNN), models and build in entity extraction component inside DIET architecture. Obtained entity tags fed to the Dialogue Management module as features. All NLU set-ups were followed by the Dialogue Management module that contains a Rule-based Policy to handle FAQs and chitchats as well as a Transformer Embedding Dialogue (TED) Policy to handle more complex and unexpected dialogue inputs. As a result, we suggest a DMS pipeline for a financial assistant, which is capable of identifying intentions, named entities, and a language of text followed by policies that allow generating a proper response (based on the designed dialogues) and suggesting the best next action.

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

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.

An Interactive Dialogue Management System for Spoken Language

The spoken dialogue system is one of the most important human-machine communication ways. Human-machine communication can be described as an interaction between the user and the computer. This field is full of research points, so it is considered a good attractive environment for many researchers. The spoken dialogue system is of great importance in the process of communicating commercial applications, and facilitating the connecting process between the human and machine which may take different faces. The main objective of this research will be building an interactive dialogue management system for spoken dialogue system in an ideal way, By answering the following main question: How can we build an interactive dialogue management system for spoken dialogue system in an ideal way has the ability to accomplish the Naturalness, Usability, Mixed initiative, Co-operativity, Robustness, and Exploration. This research will be a mixed-method research and will adopt a descriptive survey design in collecting information by Survey questionnaires, Interviews to a sample of the target population, and while secondary data will be found from books, journals, and The Internet. The most important conclusion of the research is the spoken dialogue system is to be less complexity and use uncertainty model; this way must be acceptable by the user and the system itself.