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Erschienen in:
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Establishing Strong Baselines For TripClick Health Retrieval

verfasst von : Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the re-ranking task of TripClick, which were not achieved with the original baselines. Furthermore, we study the impact of different domain-specific pre-trained models on TripClick. Finally, we show that dense retrieval outperforms BM25 by considerable margins, even with simple training procedures.

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Vorheriges Kapitel Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions
Nächstes Kapitel Less is Less: When are Snippets Insufficient for Human vs Machine Relevance Estimation?
Fußnoten
1
The TripDatabase allows users to use different ranking schemes, such as popularity, source quality and pure relevance, as well as filtering results by facets. Unfortunately, this information is not available in the public dataset.
 
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Metadaten
Titel
Establishing Strong Baselines For TripClick Health Retrieval
verfasst von
Sebastian Hofstätter
Sophia Althammer
Mete Sertkan
Allan Hanbury
Copyright-Jahr
2022
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_17