Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions

TL;DR

This paper presents a novel deep learning approach using contextualized embeddings to improve semantic similarity detection for Arabic questions across dialects, outperforming existing methods on benchmark datasets.

Contribution

Introduces a new system leveraging ELMo embeddings and a pairwise similarity layer for Arabic question similarity, effective across dialects and trained only on MSA data.

Findings

Achieved 93% F1-score on MSA benchmark

Achieved 82% F1-score on dialectal Arabic benchmark

Outperforms state-of-the-art methods in Arabic question similarity

Abstract

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.