42 citations · 185 across the 46 of their papers we have counts for
8 papers · 2 filters
Named Entity and Relation Extraction with Multi-Modal Retrieval
Xinyu Wang, Jiong Cai, Yong Jiang +3
Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE. Most existing efforts l…
Differentiable Data Augmentation for Contrastive Sentence Representation Learning
Tianduo Wang, Wei Lu
Fine-tuning a pre-trained language model via the contrastive learning framework with a large amount of unlabeled sentences or labeled sentence pairs is a common way to obtain high-…
Better Few-Shot Relation Extraction with Label Prompt Dropout
Peiyuan Zhang, Wei Lu
Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., re…
Generative Prompt Tuning for Relation Classification
Jiale Han, Shuai Zhao, Bo Cheng +2
Using prompts to explore the knowledge contained within pre-trained language models for downstream tasks has now become an active topic. Current prompt tuning methods mostly conver…
Unsupervised Non-transferable Text Classification
Guangtao Zeng, Wei Lu
Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property. To prevent the neura…
Implicit N-grams Induced by Recurrence
Xiaobing Sun, Wei Lu
Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP) tasks, recent studies reveal that they have limita…