ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model
Raki Lachraf, El Moatez Billah Nagoudi, Youcef Ayachi, Ahmed Abdelalí, Didier Schwab · 2019
Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas.In this paper, we propose an open source ArbEngVec which provides several Arabic-English cross-lingual word embedding models.To train our bilingual models, we use a large dataset with more than 93 million pairs of Arabic-English parallel sentences.In addition, we perform both extrinsic and intrinsic evaluations for the different word embedding model variants.The extrinsic evaluation assesses the performance of models on the cross-language Semantic Textual Similarity (STS), while the intrinsic evaluation is based on the Word Translation (WT) task.