Classification and Analysis of Arabic Natural Language Inference Systems
Mabrouka Ben-Sghaier, Wided Bakari, Mahmoud Néji
Abstract
In natural language, the same meaning can be expressed by different texts. The process of determining the inference relationship occurring between a text T and a hypothesis H is called Natural Language Inference (NLI). The NLI task aims to provide a generic framework that captures, in a unifying manner, the inference across Natural Language Processing applications such as question answering, summarization, information retrieval, and machine translation. Many tasks and datasets have been created to support the development and evaluation of the ability of the NLI task in different languages. For the Arabic language, interest in this field is gradually increasing. This paper aims to provide an overview of state-of-the-art NLI approaches for Arabic, the relevant knowledge resources and the used tools in order to support a better understanding of this growing field. Moreover, this paper points to classify the proposed approaches for Arabic NLI and compare existing NLI systems.