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We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e.g., M-BERT).
Roberta: A robustly optimized BERT pretraining approach
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Entities as experts: Sparse memory access with entity supervision
Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, and Tom Kwiatkowski. 2020 · 2020
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Don’t use English dev: On the zero-shot cross-lingual evaluation of contextual embeddings
Phillip Keung, Yichao Lu, Julian Salazar, and Vikas Bhardwaj. 2020 · 2020
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From zero to hero: On the limitations of zero-shot language transfer with multilingual Transformers
Anne Lauscher, Vinit Ravishankar, Ivan Vulić, and Goran Glavaš. 2020 · 2020
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Enriching BERT with Knowledge Graph Embedding for Document Classification
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Neural attentive bag-of-entities model for text classification
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Unsupervised cross-lingual representation learning at scale
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E-BERT: Efficient-yet-effective entity embeddings for BERT
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Overview of shinra2020-ml task
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Wikipedia2Vec: An efficient toolkit for learning and visualizing the embeddings of words and entities from Wikipedia
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KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
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