Papers with BIER
UR2N: Unified Retriever and ReraNker (2025.coling-industry)
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| Challenge: | XTR-style retrieval on top of trained Mono-T5 reranker is suboptimal for two-stage retrieval, arguing that it is sub-optimal. |
| Approach: | They propose a unified encoder-decoder architecture with a novel training regimen which enables the encoder representation to be used for retrieval and the decoder for re-ranking within a single unified model. |
| Outcome: | The proposed architecture outperforms ColBERT, XTR, and even serves as a superior reranker compared to the Mono-T5 re-ranker. |
Biomedical Interpretable Entity Representations (2021.findings-acl)
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| Challenge: | Existing work on general interpretable representation learning does not transfer to biomedicine . pre-trained models induce dense entity representations but are not immediately interpretable. |
| Approach: | They propose a method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. |
| Outcome: | The proposed model performs well on biomedical tasks including disambiguation and label classification. |