Prompt tuning, which only tunes continuous prompts with a frozen language model,
substantially reduces per-task storage and memory usage at training. However, in the …

Named entity recognition (NER) is a fundamental task in natural language processing.
Recent works treat named entity recognition as a reading comprehension task, constructing …

Information Extraction (IE) aims to extract structural information from unstructured texts. In
practice, long-tailed distributions caused by the selection bias of a dataset, may lead to …

Dependency parse trees are helpful for discovering the opinion words in aspect-based
sentiment analysis (ABSA). However, the trees obtained from off-the-shelf dependency …

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the
linguistic gap by training on pseudo-labeled target-language data. However, due to sub …

Conversational emotion recognition represents an important machine-learning problem with
a wide variety of deployment possibilities. The key challenge in this area is how to properly …

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 …

Named entity recognition (NER) is a fundamental and important task in NLP, aiming at
identifying named entities (NEs) from free text. Recently, since the multi-head attention …

Cross-lingual named entity recognition (NER) suffers from data scarcity in the target
languages, especially under zero-shot settings. Existing translate-train or knowledge …

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite …