49 citations · 109 across the 25 of their papers we have counts for
6 papers · 1 filter
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Runxin Xu, Fuli Luo, Chengyu Wang +4
Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quanti…
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang +4
Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus aris…
An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Peiyi Wang, Runxin Xu, Tianyu Liu +4
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce dom…
Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification
Peiyi Wang, Runxin Xu, Tianyu Liu +3
Few-Shot Event Classification (FSEC) aims at developing a model for event prediction, which can generalize to new event types with a limited number of annotated data. Existing FSEC…
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Peiyi Wang, Tianyu Liu, Damai Dai +3
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divi…
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Runxin Xu, Tianyu Liu, Lei Li +1
Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the tar…