Multilingual and Cross-cultural interactions for context-aware, and bias-controlled dialogue systems for safety-critical applications

Multilingvální a mezikulturní interakce v dialogových systémech pro bezpečnostně kritické aplikace závislé na kontextu a kontrolou zaujatosti

grant

Human computer interaction and interface, visualization and natural language, artificial intelligence, intelligence systems, multi agents systems, natural language processing, data protection and privacy, machine learning, statistical data processing and applications using data processing, formal, cognitive, functional and computational linguistics, distributed and federated adaptation of Large Language Models, Multilinguality, Multimodality, Human-in-the-loop, Bias mitigation, Grounding.

ELOQUENCE aims to research and develop new technologies supporting collaborative voice/chat bots for both low secure (low risk) and highly secure (high risk) applications. Dialogue engines powered by voice assistants have already been present in various commercial/governmental applications with lower or higher level complexities. In both cases, this complexity can be translated to a problem of analysing unstructured dialogues. Key objective of ELOQUENCE is to understand unstructured dialogues and conduct them in an explainable, safe, knowledge-grounded, trustworthy and unbiased way, while considering and building on top of prior achievements in this domain (e.g. recently launched chatGPT Large Language Models (LLMs). While including key industrial enterprises from Europe in this project (i.e. Omilia, Telefonica. ...) will approach safety with human-in-the-loop for safety-critical applications (i.e., emergency services) and via information retrieval and fact-checking against an online knowledge base for less critical autonomous systems (i.e., home-assistants). ELOQUENCE will target the R&D of these novel conversational AI technologies in multilingual and multimodal environments. Both basic research and its direct deployment through two pilots will be targeted: 1) emergency call contact centres and 2) smart assistants through decentralised training in smart homes.

Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT) , research leader
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT) , team leader
Schwarz Petr, Ing., Ph.D. (DCGM FIT BUT) , team leader
Beneš Karel, Ing. (DCGM FIT BUT)
Fajčík Martin, Ing., Ph.D. (DCGM FIT BUT)
Heřmanský Hynek, prof. Ing., Dr.Eng. (DCGM FIT BUT)
Kesiraju Santosh (DCGM FIT BUT)
Peng Junyi, Msc. Eng. (DCGM FIT BUT)
Pešán Jan, Ing. (DCGM FIT BUT)
Rohdin Johan A., Dr. (DCGM FIT BUT)
Sarvaš Marek, Bc. (DCGM FIT BUT)
Sedláček Šimon, Ing. (DCGM FIT BUT)
Yusuf Bolaji (DCGM FIT BUT)