A simple method for domain adaptation of sentence embeddings
Abstract
Pre-trained sentence embeddings have been shown to be very useful for a variety of NLP tasks. Due to the fact that training such embeddings requires a large amount of data, they are commonly trained on a variety of text data. An adaptation to specific domains could improve results in many cases, but such a finetuning is usually problem-dependent and poses the risk of over-adapting to the data used for adaptation. In this paper, we present a simple universal method for finetuning Google's Universal Sentence Encoder (USE) using a Siamese architecture. We demonstrate how to use this approach for a variety of data sets and present results on different data sets representing similar problems. The approach is also compared to traditional finetuning on these data sets. As a further advantage, the approach can be used for combining data sets with different annotations. We also present an embedding finetuned on all data sets in parallel.
Cite
@article{arxiv.2008.11228,
title = {A simple method for domain adaptation of sentence embeddings},
author = {Anna Kruspe},
journal= {arXiv preprint arXiv:2008.11228},
year = {2020}
}