Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

In this article, we introduce and discuss the pervasive issue of bias in the large language
models that are currently at the core of mainstream approaches to Natural Language …

We present simple BERT-based models for relation extraction and semantic role labeling. In
recent years, state-of-the-art performance has been achieved using neural models by …

Graph neural networks (GNNs) have become a popular approach to integrating structural
inductive biases into NLP models. However, there has been little work on interpreting them …

We introduce a globally normalized transition-based neural network model that achieves
state-of-the-art part-of-speech tagging, dependency parsing and sentence compression …

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines
integrating several different modules or components, and exploit graph recategorization, ie …

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 …

Abstract LTP (Language Technology Platform) is an integrated Chinese processing platform
which includes a suite of high performance natural language processing (NLP) modules and …

Distributed word representations (word embeddings) have recently contributed to
competitive performance in language modeling and several NLP tasks. In this work, we train …

We assume that the tokenization of a sentence is fixed and known at parsing time. That is to
say that dependency parsers will always operate on a pre-tokenized input and are not …