Setha, I., & Aliane, H. Bilingual Terminology Alignment Using Contextualized Embeddings.
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Abstract
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This work proposes a new method for terminology alignment from a comparable corpus (Arabic/French languages) for the Algerian culture field and aims to improve bilingual alignment based on contextual information of a term and to create a significant term bank i.e. a bilingual Arabic-French dictionary. Terminology Alignment faces big challenges in NLP because of the dynamic nature of terms. Fortunately, over these last few years, Deep Learning models showed very good progress with several NLP tasks such as multilingual data resourcing, glossary building, terminology understanding. . . etc. In this work, we propose a new method for terminology alignment from a comparable corpus (Arabic/French languages) for the Algerian culture field. We aim to improve bilingual alignment based on contextual information of a term and to create a significant term bank i.e. a bilingual Arabic-French dictionary. We propose to create word embeddings for both Arabic and French languages using ELMO model focusing on contextual features of terms. Then, we mapp those embeddings using Seq2seq model. We use multilingual-BERT and All-MiniLM-L6 as baseline mod- els to compare terminology alignment results. Lastly we study the performance of these models by applying evaluation methods. Experimentation’s showed quite satisfying alignment results.
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