Knowledge Based Transformer Model for Information Retrieval
Résumé
Vocabulary mismatch is a frequent problem in information retrieval (IR). It can occur when the query is short and/or ambiguous but also in specialized domains where queries are made by non-specialists and documents are written by experts. Recently, vocabulary mismatch has been addressed with neural learning-to-rank (NLTR) models and word embeddings to avoid relying only on the exact matching of terms for retrieval. Another approach to vocabulary mismatch is to use knowledge bases (KB) that can associate different terms to the same concept. Given the recent success of transformer encoders for NLP, we propose KTRel: a NLTR model that uses word embeddings, Knowledge bases and Transformer encoders for IR.
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