ERNIE: Enhanced Representation through Knowledge Integration

TL;DR

ERNIE is a knowledge-enhanced language model that improves natural language understanding by incorporating entity and phrase masking strategies, achieving state-of-the-art results on multiple Chinese NLP tasks.

Contribution

Introduces ERNIE, a novel language model that integrates knowledge through specialized masking strategies, enhancing representation learning beyond BERT.

Findings

Outperforms baseline models on five Chinese NLP tasks

Achieves new state-of-the-art results in multiple NLP benchmarks

Demonstrates superior knowledge inference capacity in cloze tests

Abstract

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words.Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit.Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test.