A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem
This work addresses query relevance for Arabic-language search engines, but appears incremental as it applies an existing NLP method to a new domain.
The paper tackles the problem of improving search engine relevance by reformulating query rewriting as a monolingual machine translation task, specifically for Arabic user queries, and describes a pipeline that maps queries to web page titles.
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.