  One of the most important challenges for modern search engines is to retrieve
relevant web content based on user queries. In order to achieve this challenge,
search engines have a module to rewrite user queries. That is why modern web
search engines utilize some statistical and neural models used in the natural
language processing domain. Statistical machine translation is a well-known NLP
method among them. The paper proposes a query rewriting pipeline based on a
monolingual machine translation model that learns to rewrite Arabic user search
queries. This paper also describes preprocessing steps to create a mapping
between user queries and web page titles.
