Ye Yuan


PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen | Chenguang Wang | Ye Yuan | Jiawei Han | Heng Ji | Koushik Sen | Ming Zhang | Dawn Song
Findings of the Association for Computational Linguistics: EMNLP 2022

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a “fill-in-the-blank” task, and introducing a parameter-lite encoder on top of the original LMs. We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-the-art approaches. For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.