IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages
Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably.However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets.In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information.At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query.Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while