BERT meets Cranfield: Uncovering the Properties of Full Ranking on Fully Labeled Data (2021.eacl-srw)
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| Challenge: | Existing information retrieval models based on pre-trained BERT models have been tested on data collections with partial relevance labels, where a relevant document has not been exposed to the annotators. |
| Approach: | They propose to use BERT-based rankers to evaluate documents with partial relevance labels on a Cranfield collection, which comes with full relevance judgment on all documents in the collection. |
| Outcome: | The proposed model performs better than the initial ranker and re-ranker on the Cranfield dataset. |
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