Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

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 …

Dialogue-level dependency parsing, despite its growing academic interest, often encounters
underperformance issues due to resource shortages. A potential solution to this challenge is …

Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled
data to improve model performance in downstream natural language processing (NLP) …

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error
correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research …

Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has
achieved promising results since these pre-trained models embed generic knowledge …

Neural constituency parsers have reached practical performance on news-domain
benchmarks. However, their generalization ability to other domains remains weak. Existing …

Dependency parsing aims to extract syntactic dependency structure or semantic
dependency structure for sentences. Existing methods for dependency parsing include …

Reward models (RM) capture the values and preferences of humans and play a central role
in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large …

In recent years, parsing performance is dramatically improved on in-domain texts thanks to
the rapid progress of deep neural network models. The major challenge for current parsing …