Enhancing Translation Language Models with Word Embedding for Information Retrieval

Abstract: In this paper, we explore the usage of Word Embedding semantic resources for Information Retrieval (IR) task. This embedding, produced by a shallow neural network, have been shown to catch semantic similarities between words (Mikolov et al., 2013). Hence, our goal is to enhance IR LLMs by addressing the term mismatch problem. To do so, we applied the model presented in the paper Integrating and Evaluating Neural Word Embedding in Information Retrieval by Zuccon et al. (2015) that proposes to estimate the translation probability of a Translation LLM using the cosine similarity between Word Embedding. The results we obtained so far did not show a statistically significant improvement compared to classical LLM.