# A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem

arXiv: https://arxiv.org/abs/2310.13031 · HTML: https://mlsanity.com/paper/2310.13031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning](https://mlsanity.com/paper/2112.08558/) (2021)
- [Contextualized Query Embeddings for Conversational Search](https://mlsanity.com/paper/2104.08707/) (2021)

## Abstract (arXiv)

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.