Two interlocking research questions of growing interest and importance in privacy research
are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact …

Text data augmentation is an effective strategy for overcoming the challenge of limited
sample sizes in many natural language processing (NLP) tasks. This challenge is especially …

Existing work in translation demonstrated the potential of massively multilingual machine
translation by training a single model able to translate between any pair of languages …

We propose Future Discriminators for Generation (FUDGE), a flexible and modular method
for controlled text generation. Given a pre-existing model G for generating text from a …

Recent work has presented intriguing results examining the knowledge contained in
language models (LMs) by having the LM fill in the blanks of prompts such as “Obama is a …

Recent works have shown that language models (LM) capture different types of knowledge
regarding facts or common sense. However, because no model is perfect, they still fail to …

Large transformer-based language models (LMs) trained on huge text corpora have shown
unparalleled generation capabilities. However, controlling attributes of the generated …

Language model based pre-trained models such as BERT have provided significant gains
across different NLP tasks. In this paper, we study different types of transformer based pre …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

Although BERT and its variants have reshaped the NLP landscape, it still remains unclear
how best to derive sentence embeddings from such pre-trained Transformers. In this work …