Natural Language Inference for Arabic using Recurrent Neural Network and Word Embedding

Abstract

The Natural Language Inference (NLI) task is a field of Natural Language Processing (NLP) in which researchers try to find ways to decide about the inference relation between two sentences. In this paper, we present an early version of an Arabic-oriented NLI system based on the word embedding technique and a deep learning model, namely Bi-LSTM. In this paper, we provide a general description of the system's various components as well as results from the parts that have already been implemented. Our primary experiments yielded positive results.