Abstract
Recently, many neural retrieval models have been proposed and shown competitive results. In particular, interaction-based models have shown superior performance to traditional models in a number of studies. However, the interactions used as the basic matching signals are between single terms or their embeddings. In reality, a term can often match a phrase or even longer segment of text. This paper proposes a Cross-Level Matching Model which enhances the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.
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Nie, Y., Nie, JY. (2020). Cross-Level Matching Model for Information Retrieval. In: Wang, F., et al. Information Retrieval Technology. AIRS 2019. Lecture Notes in Computer Science(), vol 12004. Springer, Cham. https://doi.org/10.1007/978-3-030-42835-8_10
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DOI: https://doi.org/10.1007/978-3-030-42835-8_10
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