11 citations · 34 across the 6 of their papers we have counts for
7 papers
Metric Learning and Adaptive Boundary for Out-of-Domain Detection
Petr Lorenc, Tommaso Gargiani, Jan Pichl +4
Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the…
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios
Petr Lorenc, Ana-Sabina Uban, Paolo Rosso +1
The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on…
Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization
Jakub Konrád, Jan Pichl, Petr Marek +5
The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourt…
Joint model for intent and entity recognition
Petr Lorenc
The semantic understanding of natural dialogues composes of several parts. Some of them, like intent classification and entity detection, have a crucial role in deciding the next s…
Text Summarization of Czech News Articles Using Named Entities
Petr Marek, Štěpán Müller, Jakub Konrád +3
The foundation for the research of summarization in the Czech language was laid by the work of Straka et al. (2018). They published the SumeCzech, a large Czech news-based summariz…
Do We Need Online NLU Tools?
Petr Lorenc, Petr Marek, Jan Pichl +2
The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many…