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) – research leader
Beneš Karel, Ing. (DCGM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Kesiraju Santosh, Ph.D. (DCGM)
Schwarz Petr, Ing., Ph.D. (DCGM)