Jai Gupta
2022
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
Kai Hui
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Honglei Zhuang
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Tao Chen
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Zhen Qin
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Jing Lu
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Dara Bahri
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Ji Ma
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Jai Gupta
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Cicero Nogueira dos Santos
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Yi Tay
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Donald Metzler
Findings of the Association for Computational Linguistics: ACL 2022
State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradigm or an encoder-decoder (like T5) approach. These paradigms, however, are not without flaws, i.e., running the model on all query-document pairs at inference-time incurs a significant computational cost. This paper proposes a new training and inference paradigm for re-ranking. We propose to finetune a pretrained encoder-decoder model using in the form of document to query generation. Subsequently, we show that this encoder-decoder architecture can be decomposed into a decoder-only language model during inference. This results in significant inference time speedups since the decoder-only architecture only needs to learn to interpret static encoder embeddings during inference. Our experiments show that this new paradigm achieves results that are comparable to the more expensive cross-attention ranking approaches while being up to 6.8X faster. We believe this work paves the way for more efficient neural rankers that leverage large pretrained models.