195 citations · 936 across the 38 of their papers we have counts for
6 papers · 1 filter
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning
Huijuan Wang, Siming Dai, Weiyue Su +7
Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs,…
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Shuohuan Wang, Yu Sun, Yang Xiang +26
Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models c…
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Yu Sun, Shuohuan Wang, Shikun Feng +19
Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-train…
Distilling Knowledge from Pre-trained Language Models via Text Smoothing
Xing Wu, Yibing Liu, Xiangyang Zhou +1
This paper studies compressing pre-trained language models, like BERT (Devlin et al.,2019), via teacher-student knowledge distillation. Previous works usually force the student mod…
How to Evaluate the Next System: Automatic Dialogue Evaluation from the Perspective of Continual Learning
Lu Li, Zhongheng He, Xiangyang Zhou +1
Automatic dialogue evaluation plays a crucial role in open-domain dialogue research. Previous works train neural networks with limited annotation for conducting automatic dialogue…
RLTM: An Efficient Neural IR Framework for Long Documents
Chen Zheng, Yu Sun, Shengxian Wan +1
Deep neural networks have achieved significant improvements in information retrieval (IR). However, most existing models are computational costly and can not efficiently scale to l…