Exploring Sentence Embedding Representation For Arabic Question Answering

Abstract

Question Answering Systems (QAS) are made to automatically provide accurate response to user questions that are phrased in natural language. Most of the existing QAS adopting traditional representations like word embedding and bag-of-words, have shown promising results. However, only a few works take into account the contextual information and meaning within texts to extract answers from huge sources of information. Moreover, dealing with Arabic open-domain question-answering systems is still challenging due to its rich morphology and ambiguity of words. To address these limitations, we introduce, in this study, a novel QA approach for the Arabic language that is based on passage retrieval and Sentence Embedding (SE) representation. It consists of three steps: (1) Question classification and query formulation, (2) Documents and passages retrieval, and (3) Answers extraction. In this work, we make use of the AraBERT transformer model to compute vector representation. This allows for considering both implicit semantics and the words' context within the text. Furthermore, in order to collect potential passages for user questions, we investigate a method for retrieving Arabic passages using the BM25 model, a query expansion process, and SE representation. The final answer is generated by fine-tuning AraBERT parameters and ranking passages so that the most relevant ones can be extracted. To assess our approach, we carried out several experiments on CLEF and TREC datasets using two different taxonomies. The obtained results show that the proposed method achieves 92% in terms of F1-score.

AraFast: Developing and Evaluating a Comprehensive Modern Standard Arabic Corpus for Enhanced Natural Language Processing

“…The inefficacy of this approach was primarily attributed to the inherent complexities of Arabic, a language characterized by rich morphological structures and a plethora of syntactic intricacies [67]. The absence of vowels in written text, the prevalence of morphology, the use of diacritics, and dots on letters such as ‫"ﻱ"‬ Hamzah "significantly complicate Arabic language processing tasks [69]. Figure 6 shows the loss curve of training the proposed model with the segmented Arabic corpus.…”