Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

The success of the large neural language models on many NLP tasks is exciting. However,
we find that these successes sometimes lead to hype in which these models are being …

Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

Contextual word representations derived from large-scale neural language models are
successful across a diverse set of NLP tasks, suggesting that they encode useful and …

This paper explores the knowledge of linguistic structure learned by large artificial neural
networks, trained via self-supervision, whereby the model simply tries to predict a masked …

Pretrained contextualized embeddings are powerful word representations for structured
prediction tasks. Recent work found that better word representations can be obtained by …

We propose an attention-based model that treats AMR parsing as sequence-to-graph
transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic …

While syntactic dependency annotations concentrate on the surface or functional structure of
a sentence, semantic dependency annotations aim to capture between-word relationships …

The last few years have seen a surge in the number of accurate, fast, publicly available
dependency parsers. At the same time, the use of dependency parsing in NLP applications …

A significant amount of information in today's world is stored in structured and semi-
structured knowledge bases. Efficient and simple methods to query them are essential and …