PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
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Abstract (arXiv)
This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 18 May 2022.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
- 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
- 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
- 2019Document Expansion by Query Predictioncited by 231
- 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
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