activity
20142022
most citedJoint Representation Learning of Text and Knowledge for Knowledge Graph Completion

39 citations · 126 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CL2022

Prompt Tuning for Discriminative Pre-trained Language Models

Yuan Yao, Bowen Dong, Ao Zhang +6

Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks. However, to the best of ou…

cs.LG2022

A Roadmap for Big Model

Sha Yuan, Hanyu Zhao, Shuai Zhao +97

With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…

cs.CL202221 cited

Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Ning Ding, Yujia Qin, Guang Yang +17

Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…

cs.CL2022

Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information

Fanchao Qi, Chuancheng Lv, Zhiyuan Liu +3

In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been su…

cs.CL201626 cited

Neural Emoji Recommendation in Dialogue Systems

Ruobing Xie, Zhiyuan Liu, Rui Yan +1

Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smo…

cs.CL201639 cited

Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion

Xu Han, Zhiyuan Liu, Maosong Sun

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a nov…