ERNIE: Enhanced Representation through Knowledge Integration
Published in
arXiv.org(2019)External Links:
TL
TL;DR
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.
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.
References23 items
1
Multi-Task Deep Neural Networks for Natural Language Understanding
2
Cross-lingual Language Model Pretraining
3
LCQMC:A Large-scale Chinese Question Matching Corpus
4
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
5