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README.md

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----
-annotations_creators:
-- other
-- clicks
-language_creators:
-- other
-language:
-- en-US
-license:
-- apache-2.0
-multilinguality:
-- monolingual
-pretty_name: tripclick-training
-size_categories:
-- unknown
-source_datasets: [tripclick]
-task_categories:
-- text-retrieval
-task_ids:
-- document-retrieval
----
-
-# TripClick Baselines with Improved Training Data
-
-*Establishing Strong Baselines for TripClick Health Retrieval* Sebastian Hofstätter, Sophia Althammer, Mete Sertkan and Allan Hanbury
-
-https://arxiv.org/abs/2201.00365
-
-**tl;dr** We create strong re-ranking and dense retrieval baselines (BERT<sub>CAT</sub>, BERT<sub>DOT</sub>, ColBERT, and TK) for TripClick (health ad-hoc retrieval). We improve the – originally too noisy – training data with a simple negative sampling policy. We achieve large gains over BM25 in the re-ranking and retrieval setting on TripClick, which were not achieved with the original baselines. We publish the improved training files for everyone to use.
-
-If you have any questions, suggestions, or want to collaborate please don't hesitate to get in contact with us via [Twitter](https://twitter.com/s_hofstaetter) or mail to s.hofstaetter@tuwien.ac.at
-
-**Please cite our work as:**
-````
-@misc{hofstaetter2022tripclick,
-      title={Establishing Strong Baselines for TripClick Health Retrieval}, 
-      author={Sebastian Hofst{\"a}tter and Sophia Althammer and Mete Sertkan and Allan Hanbury},
-      year={2022},
-      eprint={2201.00365},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR}
-}
-````
-
-## Published Training Files
-
-We publish the improved training files without the text content instead using the ids from TripClick (with permission from the TripClick owners); for the text content please get the full TripClick dataset from [the TripClick Github page](https://github.com/tripdatabase/tripclick). 
-
-Our training file **improved_tripclick_train_triple-ids.tsv** has the format ``query_id pos_passage_id neg_passage_id`` (with tab separation).
-
-----
-
-For more information on how to use the training files see: https://github.com/sebastian-hofstaetter/tripclick

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