  String representation Learning (SRL) is an important task in the field of
Natural Language Processing, but it remains under-explored. The goal of SRL is
to learn dense and low-dimensional vectors (or embeddings) for encoding
character sequences. The learned representation from this task can be used in
many downstream application tasks such as string similarity matching or lexical
normalization. In this paper, we propose a new method for to train a SRL model
by only using synthetic data. Our approach makes use of Contrastive Learning in
order to maximize similarity between related strings while minimizing it for
unrelated strings. We demonstrate the effectiveness of our approach by
evaluating the learned representation on the task of string similarity
matching. Codes, data and pretrained models will be made publicly available.
