A comprehensive survey on relation extraction: Recent advances and new frontiers
Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …
content. RE serves as the foundation for many natural language processing (NLP) and …
[HTML][HTML] Pre-trained models: Past, present and future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …
great success and become a milestone in the field of artificial intelligence (AI). Owing to …
[HTML][HTML] Ptr: Prompt tuning with rules for text classification
Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …
Few-nerd: A few-shot named entity recognition dataset
Cline: Contrastive learning with semantic negative examples for natural language understanding
Despite pre-trained language models have proven useful for learning high-quality semantic
representations, these models are still vulnerable to simple perturbations. Recent works …
representations, these models are still vulnerable to simple perturbations. Recent works …
Prototypical verbalizer for prompt-based few-shot tuning
Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in
few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question …
few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question …
Autoalign: fully automatic and effective knowledge graph alignment enabled by large language models
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of
entities from two different KGs that represent the same entity. Many machine learning-based …
entities from two different KGs that represent the same entity. Many machine learning-based …
Refining sample embeddings with relation prototypes to enhance continual relation extraction
Continual learning has gained increasing attention in recent years, thanks to its biological
interpretation and efficiency in many real-world applications. As a typical task of continual …
interpretation and efficiency in many real-world applications. As a typical task of continual …
Prompt-based meta-learning for few-shot text classification
Abstract Few-shot Text Classification predicts the semantic label of a given text with a
handful of supporting instances. Current meta-learning methods have achieved satisfying …
handful of supporting instances. Current meta-learning methods have achieved satisfying …