IR models using a pretrained language model significantly outperform lexical
approaches like BM25. In particular, SPLADE, which encodes texts to sparse
vectors, is an effective model for practical use because it shows robustness to
out-of-domain datasets. However, SPLADE still struggles with exact matching of
low-frequency words in training data. In addition, domain shifts in vocabulary
and word frequencies deteriorate the IR performance of SPLADE. Because
supervision data are scarce in the target domain, addressing the domain shifts
without supervision data is necessary. This paper proposes an unsupervised
domain adaptation method by filling vocabulary and word-frequency gaps. First,
we expand a vocabulary and execute continual pretraining with a masked language
model on a corpus of the target domain. Then, we multiply SPLADE-encoded sparse
vectors by inverse document frequency weights to consider the importance of
documents with lowfrequency words. We conducted experiments using our method on
datasets with a large vocabulary gap from a source domain. We show that our
method outperforms the present stateof-the-art domain adaptation method. In
addition, our method achieves state-of-the-art results, combined with BM25.

本文提出了一种使用预训练语言模型的信息检索模型 SPLADE，介绍了它在处理实际场景中的鲁棒性及其存在的问题，最后提出了一种无监督领域自适应方法，通过补充语料库中的低频词汇并使用逆文档频率权重来提高检索性能。实验结果表明，该方法在具有大量词汇差距的数据集上优于当前最先进的领域自适应方法，并与 BM25 相结合取得了最新的性能结果。