Query Augmentation by Decoding Semantics from Brain Signals

Anonymous

Abstract: Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved, potentially relevant documents. If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well. We propose Brain-Aug, which enhances a query by incorporating semantic information decoded from brain signals within a query context. Brain-Aug generates the continuation of the original query with a prompt constructed with brain signal information and a ranking-oriented inference approach. Experimental results on fMRI (functional magnetic resonance imaging) datasets show that BrainAug can reformulate semantically imprecise queries, leading to improved document ranking performance. Further analysis shows that the improvement brought by brain signals is particularly notable for ambiguous queries.

Paper Type: long

Research Area: Information Retrieval and Text Mining

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English