Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval
This work addresses a specific bottleneck in pre-training for retrieval tasks, offering an incremental improvement over existing methods.
The authors tackled the problem of random masking in pre-trained language models for dense passage retrieval by proposing a retrieval-oriented masking strategy that prioritizes important tokens, resulting in improved performance on multiple benchmarks.
Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task. The Masked Language Modeling (MLM) is a major sub-task of the pre-training process. However, we found that the conventional random masking strategy tend to select a large number of tokens that have limited effect on the passage retrieval task (e,g. stop-words and punctuation). By noticing the term importance weight can provide valuable information for passage retrieval, we hereby propose alternative retrieval oriented masking (dubbed as ROM) strategy where more important tokens will have a higher probability of being masked out, to capture this straightforward yet essential information to facilitate the language model pre-training process. Notably, the proposed new token masking method will not change the architecture and learning objective of original PTM. Our experiments verify that the proposed ROM enables term importance information to help language model pre-training thus achieving better performance on multiple passage retrieval benchmarks.