# Establishing Strong Baselines for TripClick Health Retrieval

arXiv: https://arxiv.org/abs/2201.00365 · HTML: https://mlsanity.com/paper/2201.00365/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 2 Jan 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Sparse, Dense, and Attentional Representations for Text Retrieval](https://mlsanity.com/paper/2005.00181/) (2020)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation](https://mlsanity.com/paper/2010.02666/) (2020)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://mlsanity.com/paper/2104.06979/) (2021)
- [CEDR: Contextualized Embeddings for Document Ranking](https://mlsanity.com/paper/1904.07094/) (2019)
- [TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval](https://mlsanity.com/paper/2002.06275/) (2020)
- [A Proposed Conceptual Framework for a Representational Approach to Information Retrieval](https://mlsanity.com/paper/2110.01529/) (2021)
- [Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking](https://mlsanity.com/paper/2002.01854/) (2020)
- [TripClick: The Log Files of a Large Health Web Search Engine](https://mlsanity.com/paper/2103.07901/) (2021)
- [Rapidly Bootstrapping a Question Answering Dataset for COVID-19](https://mlsanity.com/paper/2004.11339/) (2020)
- [Deep Relevance Ranking Using Enhanced Document-Query Interactions](https://mlsanity.com/paper/1809.01682/) (2018)
- [Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14618/) (2021)
- [Let's measure run time! Extending the IR replicability infrastructure to include performance aspects](https://mlsanity.com/paper/1907.04614/) (2019)
- [Mitigating the Position Bias of Transformer Models in Passage Re-Ranking](https://mlsanity.com/paper/2101.06980/) (2021)
- [End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training](https://mlsanity.com/paper/2012.01414/) (2020)
- [On the Effect of Low-Frequency Terms on Neural-IR Models](https://mlsanity.com/paper/1904.12683/) (2019)
- [CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search](https://mlsanity.com/paper/2011.01580/) (2020)
- [Participation in TREC 2020 COVID Track Using Continuous Active Learning](https://mlsanity.com/paper/2011.01453/) (2020)
- [Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19](https://mlsanity.com/paper/2011.12684/) (2020)

## Built on by (most cited first, 4 of 4)

- [An Experimental Study on Pretraining Transformers from Scratch for IR](https://mlsanity.com/paper/2301.10444/) (2023)
- [Parameter-Efficient Sparse Retrievers and Rerankers using Adapters](https://mlsanity.com/paper/2303.13220/) (2023)
- [LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search](https://mlsanity.com/paper/2304.04590/) (2023)
- [Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection](https://mlsanity.com/paper/2309.06131/) (2023)

## 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.