mlsanityData to 7 Oct 2026

Establishing Strong Baselines for TripClick Health Retrieval

arXiv 2201.00365 ↗First posted 2 Jan 2022cs.IR cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 2 Jan 2022.

Builds on

29 of 42 references.

References · 29

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17 more
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Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023An Experimental Study on Pretraining Transformers from Scratch for IRcited by 3
  2. 2023Parameter-Efficient Sparse Retrievers and Rerankers using Adapterscited by 3
  3. 2023LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Searchcited by 2
  4. 2023Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selectioncited by 1