text: 474,233,1312947499141750786,6642132,Ikuya Yamada,"Our @emnlp2020 paper “LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention” is now available on arXiv! We present new pretrained contextualized representations that achieve SOTA on five datasets including SQuAD and CoNLL-2003. <LINK> LUKE is based on bidirectional Transformer, treats words and entities in a text as independent tokens, and outputs contextualized representations of them. The representations can be used to address downstream tasks similarly to BERT. <LINK> LUKE is trained using a novel pretraining task that involves predicting randomly masked words (equivalent to BERT’s masked language model) and entities in an entity-annotated corpus obtained from Wikipedia. LUKE also uses a new *entity-aware* self-attention mechanism that considers the types of tokens (words or entities) when computing attention scores. The source code and pretrained models are available at <LINK>. The documentation will be available soon!😃",https://arxiv.org/abs/2010.01057,"Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Our source code and pretrained representations are available at this https URL ","LUKE: Deep Contextualized Entity Representations with Entity-aware