Sequence to Sequence Learning for Query Expansion
This addresses query expansion for information retrieval and question-answering, but it is incremental as it applies an existing method to a new domain.
The paper tackled query expansion in information retrieval and question-answering by developing a custom engine using sequence-to-sequence algorithms, trained and tested on open datasets with sentence-embeddings-based keyword extraction, and assessed its ability to capture expanding relations in word embeddings.
Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.