Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model

Jeska Buhmann, Maxime De Bruyn, Ehsan Lotfi, Walter Daelemans


Abstract
FAQs are important resources to find information. However, especially if a FAQ concerns many question-answer pairs, it can be a difficult and time-consuming job to find the answer you are looking for. A FAQ chatbot can ease this process by automatically retrieving the relevant answer to a user’s question. We present VaccinChatNL, a Dutch FAQ corpus on the topic of COVID-19 vaccination. Starting with 50 question-answer pairs we built VaccinChat, a FAQ chatbot, which we used to gather more user questions that were also annotated with the appropriate or new answer classes. This iterative process of gathering user questions, annotating them, and retraining the model with the increased data set led to a corpus that now contains 12,883 user questions divided over 181 answers. We provide the first publicly available Dutch FAQ answering data set of this size with large groups of semantically equivalent human-paraphrased questions. Furthermore, our study shows that before fine-tuning a classifier, continued pre-training of Dutch language models with task- and/or domain-specific data improves classification results. In addition, we show that large groups of semantically similar questions are important for obtaining well-performing intent classification models.
Anthology ID:
2022.coling-1.312
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3539–3549
Language:
URL:
https://aclanthology.org/2022.coling-1.312
DOI:
Bibkey:
Cite (ACL):
Jeska Buhmann, Maxime De Bruyn, Ehsan Lotfi, and Walter Daelemans. 2022. Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3539–3549, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model (Buhmann et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.312.pdf
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