  The paraphrase identification task involves measuring semantic similarity
between two short sentences. It is a tricky task, and multilingual paraphrase
identification is even more challenging. In this work, we train a bi-encoder
model in a contrastive manner to detect hard paraphrases across multiple
languages. This approach allows us to use model-produced embeddings for various
tasks, such as semantic search. We evaluate our model on downstream tasks and
also assess embedding space quality. Our performance is comparable to
state-of-the-art cross-encoders, with only a minimal relative drop of 7-10% on
the chosen dataset, while keeping decent quality of embeddings.
