Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

This work proposes a syntax-enhanced grammatical error correction (GEC) approach
named SynGEC that effectively incorporates dependency syntactic information into the …

Traditional NLP has long held (supervised) syntactic parsing necessary for successful
higher-level semantic language understanding (LU). The recent advent of end-to-end neural …

Most neural machine translation models only rely on pairs of parallel sentences, assuming
syntactic information is automatically learned by an attention mechanism. In this work, we …

Abstract Syntax parse trees are a method of representing sentence structure and are often
used to provide models with syntax information and enhance downstream task performance …

Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-
attention network with no explicit phrase information. However, prior work on statistical …

Transformer-based neural machine translation (NMT) has recently achieved state-of-the-art
performance on many machine translation tasks. However, recent work (Raganato and …

Most neural machine translation (NMT) models only rely on parallel sentence pairs, while
the performance drops sharply in low-resource cases, as the models fail to mine the …

With the advent of pre-trained language models, many natural language processing tasks in
various languages have achieved great success. Although some research has been …

Previous studies have shown that the Abstract Meaning Representation (AMR) can improve
Neural Machine Translation (NMT). However, there has been little work investigating …