Arabic Biomedical Community Question Answering Based on Contextualized Embeddings

Yassine El Adlouni, Noureddine En Nahnahi, Said Ouatik El Alaoui, Mohammed Meknassi, Horacio Rodríguez and Nabil Alami
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Yassine El Adlouni: LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Noureddine En Nahnahi: LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Said Ouatik El Alaoui: Laboratory of Engeneering Sciences, National School of Applied Sciences, Ibn Tofail University, Keni, Morocco
Mohammed Meknassi: LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Horacio Rodríguez: Universitat Politècnica de Catalunya, Spain
Nabil Alami: LISAC Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

International Journal of Intelligent Information Technologies (IJIIT), 2021, vol. 17, issue 3, 1-17

Abstract: Community question answering has become increasingly important as they are practical for seeking and sharing information. Applying deep learning models often leads to good performance, but it requires an extensive amount of annotated data, a problem exacerbated for languages suffering a scarcity of resources. Contextualized language representation models have gained success due to promising results obtained on a wide array of downstream natural language processing tasks such as text classification, textual entailment, and paraphrase identification. This paper presents a novel approach by fine-tuning contextualized embeddings for a medical domain community question answering task. The authors propose an architecture combining two neural models powered by pre-trained contextual embeddings to learn a sentence representation and thereafter fine-tuned on the task to compute a score used for both ranking and classification. The experimental results on SemEval Task 3 CQA show that the model significantly outperforms the state-of-the-art models by almost 2% for the '16 edition and 1% for the '17 edition.

Date: 2021
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