Ma, G., Wu, X., Wang, P., & Hu, S. CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval.
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Abstract
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This work proposes to pre-train Contextual Masked Auto-Encoding with Mixture-of-Textual-Experts (CoT-MoTE), which incorporates textual-specific experts for individually encoding the distinct properties of queries and passages. Passage retrieval aims to retrieve relevant passages from large collections of the open-domain corpus. Contextual Masked Auto-Encoding has been proven effective in representation bottleneck pre-training of a monolithic dual-encoder for passage retrieval. Siamese or fully separated dual-encoders are often adopted as basic retrieval architecture in the pre-training and fine-tuning stages for encoding queries and passages into their latent embedding spaces. However, simply sharing or separating the parameters of the dual-encoder results in an imbalanced discrimination of the embedding spaces. In this work, we propose to pre-train Contextual Masked Auto-Encoder with Mixture-of-Textual-Experts (CoT-MoTE). Specifically, we incorporate textual-specific experts for individually encoding the distinct properties of queries and passages. Meanwhile, a shared self-attention layer is still kept for unified attention modeling. Results on large-scale passage retrieval benchmarks show steady improvement in retrieval performances. The quantitive analysis also shows a more balanced discrimination of the latent embedding spaces.
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