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arXiv · 2201.00365

Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter·Sophia Althammer·Mete Sertkan·Allan Hanbury
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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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Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury. 2022-01-02. Establishing Strong Baselines for TripClick Health Retrieval. https://arxiv.org/abs/2201.00365

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