Natural Language Processing

This work describes a self-supervised data augmentation approach used to improve learning models' performances when only a moderate amount of labeled data is available.

We present ETC-NLG, an approach leveraging topic modeling annotations to enable fully-unsupervised End-to-end Topic-Conditioned Natural Language Generation over emergent topics in unlabeled document collections.

A summary of promising directions from ICLR 2020 for better and faster pretrained tranformers language models.

A semantic browser for SARS-CoV-2 and COVID-19 powered by neural language models.

An overview of the latest advances in the field of NLP, with a focus on neural models and language understanding.

Discussing the applications of AI and NLP in the fields of literature and digital humanities.