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Learning-to-Rank with BERT in TF-Ranking

arXiv 2004.08476 ↗First posted 17 Apr 2020cs.IRcited by 36

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Abstract (arXiv)

This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance. This approach is proved to be effective in a public MS MARCO benchmark [3]. Our first two submissions achieve the best performance for the passage re-ranking task [4], and the second best performance for the passage full-ranking task as of April 10, 2020 [5]. To leverage the lately development of pre-trained language models, we recently integrate RoBERTa [6] and ELECTRA [7]. Our latest submissions improve our previously state-of-the-art re-ranking performance by 4.3% [8], and achieve the third best performance for the full-ranking task [9] as of June 8, 2020. Both of them demonstrate the effectiveness of combining ranking losses with BERT representations for document ranking.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2020.

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Citing papers per year: 2021: 11; 2022: 8; 2023: 6; 2024: 7; 2025: 3; 2026: 1.

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  1. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 175
  2. 2021Explaining Answers with Entailment Treescited by 148
  3. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  4. 2023Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Predictioncited by 113
  5. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 82
  6. 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
  7. 2022Entailment Tree Explanations via Iterative Retrieval-Generation Reasonercited by 26
  8. 2021Argument Undermining: Counter-Argument Generation by Attacking Weak Premisescited by 22
  9. 2022QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillationcited by 21
  10. 2022A Neural Pairwise Ranking Model for Readability Assessmentcited by 14
  11. 2022ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inferencecited by 9
  12. 2021Fairness Through Regularization for Learning to Rankcited by 8
24 more
  1. 2024MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankerscited by 7
  2. 2024Self-Calibrated Listwise Reranking with Large Language Modelscited by 7
  3. 2021On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratingscited by 5
  4. 2022SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Textscited by 5
  5. 2025Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencingcited by 5
  6. 2022Learning List-Level Domain-Invariant Representations for Rankingcited by 4
  7. 2021"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systemscited by 3
  8. 2023Explicit and Implicit Semantic Ranking Frameworkcited by 3
  9. 2024Entity Insertion in Multilingual Linked Corpora: The Case of Wikipediacited by 3
  10. 2021Open-Domain Conversational Search Assistant with Transformerscited by 2
  11. 2024Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrievalcited by 2
  12. 2025Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRScited by 2
  13. 2023Power-up! What Can Generative Models Do for Human Computation Workflows?cited by 1
  14. 2023Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcitycited by 1
  15. 2023DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completioncited by 1
  16. 2024Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotationscited by 1
  17. 2024Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domaincited by 1
  18. 2021Knowledge-driven Answer Generation for Conversational Searchcited by 0
  19. 2021Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratingscited by 0
  20. 2022Better Query Graph Selection for Knowledge Base Question Answeringcited by 0
  21. 2023Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-rankingcited by 0
  22. 2024OFER: Occluded Face Expression Reconstructioncited by 0
  23. 2025PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Predictioncited by 0
  24. 2026The Coverage Illusion: From Pre-retrieval Routing Failure to Post-retrieval Cascades in a Production RAG Systemcited by 0