  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.
