In recent years, deep learning models have been successfully applied to Information Retrieval (IR), mainly for assessing the topical relevance of documents with respect to queries. However, relevance is a multidimensional concept, which can be assessed based on several criteria, depending on the document type, the domain considered, the search task performed, etc. Given that recent advancements in deep neural networks enable several learning tasks to be solved simultaneously, in this paper we examine the possibility of modeling multidimensional relevance by jointly solving a retrieval task, to learn topical relevance, and a classification task, to learn additional relevance dimensions. To instantiate and evaluate the proposed model, we consider three query-independent relevance dimensions beyond topicality, i.e., readability, trustworthiness, and credibility. The reported findings show that the proposed joint modeling can improve the performance of the retrieval task.

Putri, D., Viviani, M., Pasi, G. (2021). A Multi-Task Learning Model for Multidimensional Relevance Assessment. In Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp.103-115). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-85251-1_9].

A Multi-Task Learning Model for Multidimensional Relevance Assessment

Putri D. G. P.;Viviani M.;Pasi G.
2021

Abstract

In recent years, deep learning models have been successfully applied to Information Retrieval (IR), mainly for assessing the topical relevance of documents with respect to queries. However, relevance is a multidimensional concept, which can be assessed based on several criteria, depending on the document type, the domain considered, the search task performed, etc. Given that recent advancements in deep neural networks enable several learning tasks to be solved simultaneously, in this paper we examine the possibility of modeling multidimensional relevance by jointly solving a retrieval task, to learn topical relevance, and a classification task, to learn additional relevance dimensions. To instantiate and evaluate the proposed model, we consider three query-independent relevance dimensions beyond topicality, i.e., readability, trustworthiness, and credibility. The reported findings show that the proposed joint modeling can improve the performance of the retrieval task.
slide + paper
Multi-Task Learning; Multidimensional relevance; Neural information retrieval;
English
International Conference of the Cross-Language Evaluation Forum for European Languages, CLEF 2021
2021
Experimental IR Meets Multilinguality, Multimodality, and Interaction
978-3-030-85250-4
2021
12880
103
115
none
Putri, D., Viviani, M., Pasi, G. (2021). A Multi-Task Learning Model for Multidimensional Relevance Assessment. In Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp.103-115). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-85251-1_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/332750
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