Dialogue Management and Language Generation for a Robust Conversational Virtual Coach: Validation and User Study

Designing human–machine interactive systems requires cooperation between different disciplines is required. In this work, we present a Dialogue Manager and a Language Generator that are the core modules of a Voice-based Spoken Dialogue System (SDS) capable of carrying out challenging, long and complex coaching conversations. We also develop an efficient integration procedure of the whole system that will act as an intelligent and robust Virtual Coach. The coaching task significantly differs from the classical applications of SDSs, resulting in a much higher degree of complexity and difficulty. The Virtual Coach has been successfully tested and validated in a user study with independent elderly, in three different countries with three different languages and cultures: Spain, France and Norway.The research presented in this paper has been conducted as part of the project EMPATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant No. 769872. Additionally, this work has been partially funded by projects BEWORD and AMIC-PC of the Minister of Science of Technology, under Grant Nos. PID2021-126061OB-C42 and PDC2021-120846-C43, respectively. Vázquez and López Zorrilla received a PhD scholarship from the Basque Government, with Grant Nos. PRE 2020 1 0274 and PRE 2017 1 0357, respectively

Towards structured neural spoken dialogue modelling.

195 p.In this thesis, we try to alleviate some of the weaknesses of the current approaches to dialogue modelling,one of the most challenging areas of Artificial Intelligence. We target three different types of dialogues(open-domain, task-oriented and coaching sessions), and use mainly machine learning algorithms to traindialogue models. One challenge of open-domain chatbots is their lack of response variety, which can betackled using Generative Adversarial Networks (GANs). We present two methodological contributions inthis regard. On the one hand, we develop a method to circumvent the non-differentiability of textprocessingGANs. On the other hand, we extend the conventional task of discriminators, which oftenoperate at a single response level, to the batch level. Meanwhile, two crucial aspects of task-orientedsystems are their understanding capabilities because they need to correctly interpret what the user islooking for and their constraints), and the dialogue strategy. We propose a simple yet powerful way toimprove spoken understanding and adapt the dialogue strategy by explicitly processing the user's speechsignal through audio-processing transformer neural networks. Finally, coaching dialogues shareproperties of open-domain and task-oriented dialogues. They are somehow task-oriented but, there is norush to complete the task, and it is more important to calmly converse to make the users aware of theirown problems. In this context, we describe our collaboration in the EMPATHIC project, where a VirtualCoach capable of carrying out coaching dialogues about nutrition was built, using a modular SpokenDialogue System. Second, we model such dialogues with an end-to-end system based on TransferLearning

Ageing in the 21st century in Europe: social challenges and innovation opportunities to support elderly independency and wellbeing.

This chapter describes the vision of the EMPATHIC project of social challenges and innovation opportunities to support elderly independency and well- being. Asanintroduction,wefirstidentifythemainchallengesthatresultfromthe current demographic status in Europe. Then, we show the vision and approach pro- posed by the EMPATHIC project to deal with some of these challenges. Next sec- tion develops the main concepts, goals and outcomes of the EMPATHIC project. The following section reports the impact of the project and the final section de- scribes the exploitation of the results as well as the concluding remarksThe research leading to the results in this paper has been conducted in the project EMPATHIC (Grant N: 769872) that received funding from the European Union’s Horizon 2020 research and innovation programme

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

A multilingual neural coaching model with enhanced long-term dialogue structure

In this work we develop a fully data-driven conversational agent capable of carrying out motivational coach- ing sessions in Spanish, French, Norwegian, and English. Unlike the majority of coaching, and in general well-being related conversational agents that can be found in the literature, ours is not designed by hand- crafted rules. Instead, we directly model the coaching strategy of professionals with end users. To this end, we gather a set of virtual coaching sessions through a Wizard of Oz platform, and apply state of the art Natural Language Processing techniques. We employ a transfer learning approach, pretraining GPT2 neural language models and fine-tuning them on our corpus. However, since these only take as input a local dialogue history, a simple fine-tuning procedure is not capable of modeling the long-term dialogue strategies that appear in coaching sessions. To alleviate this issue, we first propose to learn dialogue phase and scenario embeddings in the fine-tuning stage. These indicate to the model at which part of the dialogue it is and which kind of coaching session it is carrying out. Second, we develop a global deep learning system which controls the long-term structure of the dialogue. We also show that this global module can be used to visualize and interpret the decisions taken by the the conversational agent, and that the learnt representations are comparable to dialogue acts. Automatic and human evaluation show that our proposals serve to improve the baseline models. Finally, interaction experiments with coaching experts indicate that the system is usable and gives rise to positive emotions in Spanish, French and English, while the results in Norwegian point out that there is still work to be done in fully data driven approaches with very low resource languages.This work has been partially funded by the Basque Government under grant PRE_2017_1_0357 and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769872

Transfer learning in hierarchical dialogue topic classification with neural networks

Knowledge transfer between tasks can significantly improve the efficiency of machine learning algorithms. In supervised natural language understanding problems, this sort of improvement is critical since the availability of labelled data is usually scarce. In this paper we address the question of transfer learning between related topic classification tasks. A characteristic of our problem is that the tasks have a hierarchical relationship. Therefore, we introduce and validate how to implement the transfer exploiting this hierarchical structure. Our results for a real-world topic classification task show that the transfer can produce improvements in the behavior of the classifiers for some particular problems.The research presented in this paper is conducted as part of the project EM-PATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769872