Sequence to Sequence Learning for Query Expansion

Abstract

As fas as we are aware, using Sequence to Sequence algorithms for query expansion has not been explored yet in Information Retrieval literature. We tried to fill this gap in the literature with a custom Query Expansion system trained and tested on open datasets. One specificity of our engine compared to classic ones is that it does not need the documents to expand the introduced query. We test our expansions on two different tasks : Information Retrieval and Answer preselection. Our method yielded a slight improvement in performance in both two tasks . Our main contributions are :

• Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction.

• We assess the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings’ space.

We afterwards started a quantitative and qualitative analysis of the weights learned by our network. In the second part, I will discuss what is learned by a Recurrent Neural Network compared to what we know about human language learning.