Coreference resolution aims at resolving repeated references to an object in a document
and forms a core component of natural language processing (NLP) research. When used as …

The task of resolving repeated objects in natural languages is known as coreference
resolution, and it is an important part of modern natural language processing. It is classified …

Standard test sets for supervised learning evaluate in-distribution generalization.
Unfortunately, when a dataset has systematic gaps (eg, annotation artifacts), these …

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low
resource domains. Existing approaches only learn class-specific semantic features and …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Few-shot named entity recognition (NER) systems aim at recognizing novel-class named
entities based on only a few labeled examples. In this paper, we present a decomposed …

In this paper, we explore the slot tagging with only a few labeled support sentences (aka few-
shot). Few-shot slot tagging faces a unique challenge compared to the other few-shot …

Recent studies have shown that language models pretrained and/or fine-tuned on randomly
permuted sentences exhibit competitive performance on GLUE, putting into question the …

We present in this work a new dataset of coreference annotations for works of literature in
English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction. This dataset …

Transfer learning, particularly approaches that combine multi-task learning with pre-trained
contextualized embeddings and fine-tuning, have advanced the field of Natural Language …