Abstract We describe Abstract Meaning Representation (AMR), a semantic representation
language in which we are writing down the meanings of thousands of English sentences …

Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …

The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and
Arabic, using the final version, v5. 0, of the OntoNotes corpus. It was a follow-on to the …

Natural Language Processing (NLP) systems learn harmful societal biases that cause them
to amplify inequality as they are deployed in more and more situations. To guide efforts at …

Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …

Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art
results in a variety of Natural Language Processing tasks. However, understanding of their …

As machine learning models continue to increase in complexity, collecting large hand-
labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker …

Large-scale linguistically annotated corpora have played a crucial role in advancing the
state of the art of key natural language technologies such as syntactic, semantic and …

We present a novel benchmark and associated evaluation metrics for assessing the
performance of text anonymization methods. Text anonymization, defined as the task of …

The enormous growth and availability of data poses a great challenge for extracting useful
information from documents written in natural language. The information extraction task has …