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    <abstract>Deep learning models have significantly advanced the state of the art of question answering systems. However, the majority of datasets available for training such models have been annotated by humans, are open-domain, and are composed primarily in English. To deal with these limitations, we introduce a pipeline that creates synthetic data from natural text. To illustrate the domain-adaptability of our approach, as well as its multilingual potential, we use our pipeline to obtain synthetic data in English and Dutch. We combine the synthetic data with non-synthetic data (SQuAD 2.0) and evaluate multilingual BERT models on the question answering task. Models trained with synthetically augmented data demonstrate a clear improvement in performance when evaluated on the domain-specific test set, compared to the models trained exclusively on SQuAD 2.0. We expect our work to be beneficial for training domain-specific question-answering systems when the amount of available data is limited.</abstract>
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%0 Conference Proceedings
%T Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems
%A Kramchaninova, Alina
%A Defauw, Arne
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F kramchaninova-defauw-2022-synthetic
%X Deep learning models have significantly advanced the state of the art of question answering systems. However, the majority of datasets available for training such models have been annotated by humans, are open-domain, and are composed primarily in English. To deal with these limitations, we introduce a pipeline that creates synthetic data from natural text. To illustrate the domain-adaptability of our approach, as well as its multilingual potential, we use our pipeline to obtain synthetic data in English and Dutch. We combine the synthetic data with non-synthetic data (SQuAD 2.0) and evaluate multilingual BERT models on the question answering task. Models trained with synthetically augmented data demonstrate a clear improvement in performance when evaluated on the domain-specific test set, compared to the models trained exclusively on SQuAD 2.0. We expect our work to be beneficial for training domain-specific question-answering systems when the amount of available data is limited.
%U https://aclanthology.org/2022.eamt-1.18
%P 151-160

Markdown (Informal)

[Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems](https://aclanthology.org/2022.eamt-1.18) (Kramchaninova & Defauw, EAMT 2022)

• Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems (Kramchaninova & Defauw, EAMT 2022)

ACL

• Alina Kramchaninova and Arne Defauw. 2022. Synthetic Data Generation for Multilingual Domain-Adaptable Question Answering Systems. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 151–160, Ghent, Belgium. European Association for Machine Translation.