ADA TaLKS
Our work on Conversational Agents can be described as Adaptive Dialogue Agents using Task-oriented Learning with Knowledge and Senses.
ADA TaLKS represents the CLiPS research part of the Flanders AI Research Program focusing on conversational agents as part of the human-like AI challenge. Within our work package on conversational agents, we cooperate with the Universities of Leuven (LIIR), Brussels (AI-LAB) and Ghent (LT3 and K2T). On this page you can find short video’s on all the current research in our workpackage.
The focus of ADA TaLKS is on enabling a flexible method for incorporating and updating relevant background knowledge in a deep learning approach for dialog systems. In addition, we are researching memory encoding in order to improve consistency of the generated answers with the dialog history and the agent’s persona.
People
Publications
The Effects of Expressing Empathy/Autonomy Support Using a COVID-19 Vaccination Chatbot: Experimental Study in a Sample of Belgian Adults
Wojciech Trzebiński, Toni Claessens, Jeska Buhmann, Aurélie De Waele, Greet Hendrickx, Pierre Van Damme, Walter Daelemans, Karolien Poels
JMIR Form Res 2023
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20Q: Overlap-Free World Knowledge Benchmark for Language Models
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
GEM 2022
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Is It Smaller Than a Tennis Ball? Language Models Play the Game of Twenty Questions
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
BlackboxNLP 2022
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Machine Translation for Multilingual Intent Detection and Slots Filling
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
MMNLU 2022
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Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model
Jeska Buhmann, Maxime De Bruyn, Ehsan Lotfi and Walter Daelemans
COLING 2022
bibtex | Outstanding paper
Open-Domain Dialog Evaluation Using Follow-Ups Likelihood
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
COLING 2022
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Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models
Ehsan Lotfi, Maxime De Bruyn, Jeska Buhmann and Walter Daelemans
NLP4ConvAI 2021
bibtex | Best Paper Award
MFAQ: a Multilingual FAQ Dataset
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
MRQA 2021
bibtex | Best Paper Award
ConveRT for FAQ Answering
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
Bnaic/benelearn 2021
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BART for Knowledge Grounded Conversations
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans
KDD Converse 2020
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Proofs of Technology
VaccinChat
We developed a Dutch chatbot that answers FAQ’s on COVID-19 vaccines and the Flemish vaccination strategy: vaccinchat.be
This project also gave us insight in the evolution of user questions over time
Car insurance chatbot for Belfius
In this work different data augmentation methods were tested to lift the performance of Belfius’ less-used French car insurance bot to the level of the more frequently used Dutch system. These methods include back-translation, paraphrasing and question generation.