31 citations · 82 across the 11 of their papers we have counts for
12 papers · 1 filter
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Jianing Wang, Chengcheng Han, Chengyu Wang +5
Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, whi…
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
Jianing Wang, Chengyu Wang, Minghui Qiu +4
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-tr…
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
Ziyun Xu, Chengyu Wang, Minghui Qiu +4
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on label…
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
Minghui Qiu, Peng Li, Chengyu Wang +8
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP)…
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
Chengyu Wang, Mengli Cheng, Xu Hu +1
We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing aud…
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
Chengyu Wang, Minghui Qiu, Jun Huang +1
Pre-trained neural language models bring significant improvement for various NLP tasks, by fine-tuning the models on task-specific training sets. During fine-tuning, the parameters…