Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions
This addresses the problem of detecting duplicate questions in community platforms like Quora for Arabic speakers, which is incremental as it applies existing methods (ELMo embeddings) to a new language context.
The paper tackled the problem of identifying semantically similar questions in Arabic, which is challenging due to its under-resourced nature, dialects, and rich morphology, and achieved state-of-the-art results with 93% F1-score on a Modern Standard Arabic benchmark and 82% on a dialectical benchmark.
Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.