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Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
State-of-the-art natural language understanding classification models fo...
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Self-training and Pre-training are Complementary for Speech Recognition
Self-training and unsupervised pre-training have emerged as effective ap...
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Self-training Improves Pre-training for Natural Language Understanding
Unsupervised pre-training has led to much recent progress in natural lan...
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Unsupervised Cross-lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross-lingual speech representatio...
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Unsupervised Cross-lingual Representation Learning at Scale
This paper shows that pretraining multilingual language models at scale ...
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Emerging Cross-lingual Structure in Pretrained Language Models
We study the problem of multilingual masked language modeling, i.e. the ...
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CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Pre-training text representations have led to significant improvements i...
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Cross-lingual Language Model Pretraining
Recent studies have demonstrated the efficiency of generative pretrainin...
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XNLI: Evaluating Cross-lingual Sentence Representations
State-of-the-art natural language processing systems rely on supervision...
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Although much effort has recently been devoted to training high-quality ...
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Phrase-Based & Neural Unsupervised Machine Translation
Machine translation systems achieve near human-level performance on some...
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SentEval: An Evaluation Toolkit for Universal Sentence Representations
We introduce SentEval, a toolkit for evaluating the quality of universal...
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Word Translation Without Parallel Data
State-of-the-art methods for learning cross-lingual word embeddings have...
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Learning Visually Grounded Sentence Representations
We introduce a variety of models, trained on a supervised image captioni...
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Many modern NLP systems rely on word embeddings, previously trained in a...
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Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
We propose Meta-Prod2vec, a novel method to compute item similarities fo...
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Very Deep Convolutional Networks for Text Classification
The dominant approach for many NLP tasks are recurrent neural networks, ...
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