Deep neural networks often learn unintended bias during training, which might have harmful
effects when deployed in realworld settings. This work surveys 214 papers related to …

Recent advances in the development of large language models have resulted in public
access to state-of-the-art pre-trained language models (PLMs), including Generative Pre …

For some named entities in the Chinese finance domain that are long, with difficult to
delineate boundaries and diverse forms of expression, we propose a method based on …

Existing Natural Language Understanding (NLU) models have been shown to incorporate
dataset biases leading to strong performance on in-distribution (ID) test sets but poor …

Named Entity Recognition (NER) is a well researched NLP task and is widely used in real
world NLP scenarios. NER research typically focuses on the creation of new ways of training …

Intermediate layer matching is shown as an effective approach for improving knowledge
distillation (KD). However, this technique applies matching in the hidden spaces of two …

Recent advances in deep learning and natural language processing (NLP) have opened
many new opportunities for automatic text understanding and text processing in the medical …

Demonstration-based learning has shown impressive performance in exploiting pretrained
language models under few-shot learning settings. It is interesting to see that …

Recently, many studies have illustrated the robustness problem of Named Entity
Recognition (NER) systems: the NER models often rely on superficial entity patterns for …

We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of
datasets for low-, medium-and high-resource tasks. The total set of nine tasks includes four …