  Natural Language Inference (NLI) is a hot topic research in natural language
processing, contradiction detection between sentences is a special case of NLI.
This is considered a difficult NLP task which has a big influence when added as
a component in many NLP applications, such as Question Answering Systems, text
Summarization. Arabic Language is one of the most challenging low-resources
languages in detecting contradictions due to its rich lexical, semantics
ambiguity. We have created a data set of more than 12k sentences and named
ArNLI, that will be publicly available. Moreover, we have applied a new model
inspired by Stanford contradiction detection proposed solutions on English
language. We proposed an approach to detect contradictions between pairs of
sentences in Arabic language using contradiction vector combined with language
model vector as an input to machine learning model. We analyzed results of
different traditional machine learning classifiers and compared their results
on our created data set (ArNLI) and on an automatic translation of both PHEME,
SICK English data sets. Best results achieved using Random Forest classifier
with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively.
