activity
20182022
most citedAlquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models

12 citations · 41 across the 5 of their papers we have counts for

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Showing cs.CLShow all

9 papers · 1 filter

cs.CL2022

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…

cs.CL202111 cited

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…

cs.CL202111 cited

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…

cs.CL2021

OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

Petr Marek, Vishal Ishwar Naik, Vincent Auvray +1

Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, col…

cs.CL2020

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…

cs.CL20207 cited

Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph

Jan Pichl, Petr Marek, Jakub Konrád +3

The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular…