  Contrastive learning is widely used for sentence representation learning.
Despite this prevalence, most studies have focused exclusively on English and
few concern domain adaptation for domain-specific downstream tasks, especially
for low-resource languages like Japanese, which are characterized by
insufficient target domain data and the lack of a proper training strategy. To
overcome this, we propose a novel Japanese sentence representation framework,
JCSE (derived from ``Contrastive learning of Sentence Embeddings for
Japanese''), that creates training data by generating sentences and
synthesizing them with sentences available in a target domain. Specifically, a
pre-trained data generator is finetuned to a target domain using our collected
corpus. It is then used to generate contradictory sentence pairs that are used
in contrastive learning for adapting a Japanese language model to a specific
task in the target domain.
  Another problem of Japanese sentence representation learning is the
difficulty of evaluating existing embedding methods due to the lack of
benchmark datasets. Thus, we establish a comprehensive Japanese Semantic
Textual Similarity (STS) benchmark on which various embedding models are
evaluated. Based on this benchmark result, multiple embedding methods are
chosen and compared with JCSE on two domain-specific tasks, STS in a clinical
domain and information retrieval in an educational domain. The results show
that JCSE achieves significant performance improvement surpassing direct
transfer and other training strategies. This empirically demonstrates JCSE's
effectiveness and practicability for downstream tasks of a low-resource
language.
