  Sentence embedding is a significant research topic in the field of natural
language processing (NLP). Generating sentence embedding vectors reflecting the
intrinsic meaning of a sentence is a key factor to achieve an enhanced
performance in various NLP tasks such as sentence classification and document
summarization. Therefore, various sentence embedding models based on supervised
and unsupervised learning have been proposed after the advent of researches
regarding the distributed representation of words. They were evaluated through
semantic textual similarity (STS) tasks, which measure the degree of semantic
preservation of a sentence and neural network-based supervised embedding models
generally yielded state-of-the-art performance. However, these models have a
limitation in that they have multiple parameters to update, thereby requiring a
tremendous amount of labeled training data. In this study, we propose an
efficient approach that learns a transition matrix that refines a sentence
embedding vector to reflect the latent semantic meaning of a sentence. The
proposed method has two practical advantages; (1) it can be applied to any
sentence embedding method, and (2) it can achieve robust performance in STS
tasks irrespective of the number of training examples.
