Junxian He

Choosing Transfer Languages for Cross-Lingual Learning

2019 · arXiv (Cornell University)

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, …

On the Sentence Embeddings from Pre-trained Language Models

2020 · arXiv (Cornell University)

Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models without fine-tuning have been found to poorly capture semantic meaning of sentences. In …

Contrastive Learning of Sentence Embeddings from Scratch

2023

Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences …

A Probabilistic Formulation of Unsupervised Text Style Transfer

2020 · arXiv (Cornell University)

We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a …