In this paper, we propose a novel approach to consider multiple dimensions of relevance in cross-encoder re-ranking. On the one hand, cross-encoders constitute an effective solution for re-ranking when considering a single relevance dimension such as topicality, but are not designed to straightforwardly account for additional relevance dimensions. On the other hand, the majority of re-ranking models accounting for multdimensional relevance are often based on the aggregation of multiple relevance scores at the re-ranking stage, leading to potential compensatory effects. To address these issues, in the proposed solution we enhance the candidate documents retrieved by a first-stage lexical retrieval model with suitable relevance statements related to distinct relevance dimensions, and then perform a re-ranking on them with cross-encoders. In this work we focus, in particular, on an extra dimension of relevance beyond topicality, namely, credibility, to address health misinformation in the Consumer Health Search task. Experimental evaluations are performed by considering publicly available datasets; our results show that the proposed approach statistically outperforms state-of-the-art aggregation-based and cross-encoder re-rankers.

Upadhyay, R., Askari, A., Pasi, G., Viviani, M. (2024). Beyond Topicality: Including Multidimensional Relevance in Cross-encoder Re-ranking. In Advances in Information Retrieval 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part I (pp.262-277). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-56027-9_16].

Beyond Topicality: Including Multidimensional Relevance in Cross-encoder Re-ranking

Upadhyay, Rishabh;Pasi, Gabriella;Viviani, Marco
2024

Abstract

In this paper, we propose a novel approach to consider multiple dimensions of relevance in cross-encoder re-ranking. On the one hand, cross-encoders constitute an effective solution for re-ranking when considering a single relevance dimension such as topicality, but are not designed to straightforwardly account for additional relevance dimensions. On the other hand, the majority of re-ranking models accounting for multdimensional relevance are often based on the aggregation of multiple relevance scores at the re-ranking stage, leading to potential compensatory effects. To address these issues, in the proposed solution we enhance the candidate documents retrieved by a first-stage lexical retrieval model with suitable relevance statements related to distinct relevance dimensions, and then perform a re-ranking on them with cross-encoders. In this work we focus, in particular, on an extra dimension of relevance beyond topicality, namely, credibility, to address health misinformation in the Consumer Health Search task. Experimental evaluations are performed by considering publicly available datasets; our results show that the proposed approach statistically outperforms state-of-the-art aggregation-based and cross-encoder re-rankers.
slide + paper
Consumer Health Search; Cross-encoders; Health misinformation; Re-ranking;
English
46th European Conference on Information Retrieval, ECIR 2024 - March 24–28, 2024
2024
Goharian, N; Tonellotto, N; He, Y; Lipani, A; McDonald, G; Macdonald, C; Ounis, I
Advances in Information Retrieval 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part I
9783031560262
20-mar-2024
2024
14608 LNCS
262
277
none
Upadhyay, R., Askari, A., Pasi, G., Viviani, M. (2024). Beyond Topicality: Including Multidimensional Relevance in Cross-encoder Re-ranking. In Advances in Information Retrieval 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part I (pp.262-277). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-56027-9_16].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/469460
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