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- W4367047068 abstract "Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in representation learning towards compressive global sequence-level embeddings. However, they are prone to overlook local salient phrases and entity mentions in texts, which usually play pivot roles in first-stage retrieval. To mitigate this weakness, we propose to make a dense retriever align a well-performing lexicon-aware representation model. The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects – 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make the dense model’s behavior incline to the other. We evaluate our model on three public benchmarks, which shows that with a comparable lexicon-aware retriever as the teacher, our proposed dense one can bring consistent and significant improvements, and even outdo its teacher. In addition, we show our lexicon-aware distillation strategies are compatible with the standard ranker distillation, which can further lift state-of-the-art performance.1" @default.
- W4367047068 created "2023-04-27" @default.
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- W4367047068 date "2023-04-30" @default.
- W4367047068 modified "2023-10-16" @default.
- W4367047068 title "LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval" @default.
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- W4367047068 doi "https://doi.org/10.1145/3543507.3583294" @default.
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