Longformer for MS MARCO Document Re-ranking Task
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Abstract (arXiv)
Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard. The best performance is achieved by using transformer-based models as re-rankers, e.g., BERT. We employ Longformer, a BERT-like model for long documents, on the MS MARCO document re-ranking task. The complete code used for training the model can be found on: https://github.com/isekulic/longformer-marco
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2022Evaluating Mixed-initiative Conversational Search Systems via User Simulationcited by 24
- 2021Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistantscited by 11
- 2021A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collectionscited by 2
- 2022Evaluating Mixed-initiative Conversational Search Systems via User Simulation17 Apr 2022
- 2021A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections16 Jun 2021
- 2021Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants9 Jan 2021