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katsutan

katsutan
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Decks

What does BERT learn about the structure of language?
katsutan
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180
Simple and Effective Paraphrastic Similarity from Parallel Translations
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160
Simple task-specific bilingual word embeddings
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180
Retrofitting Contextualized Word Embeddings with Paraphrases
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210
Character Eyes: Seeing Language through Character-Level Taggers
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160
Improving Word Embeddings Using Kernel PCA
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180
Better Word Embeddings by Disentangling Contextual n-Gram Information
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250
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
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230
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
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250
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
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220
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
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200
Unsupervised Statistical Machine Translation
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150
Named Entity Recognition With Parallel Recurrent Neural Networks
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180
Phrase-level Self-Attention Networks for Universal Sentence Encoding
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210
How Transferable are Neural Networks in NLP Applications?
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200
Split and Rephrase: Better Evaluation and a Stronger Baseline
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150
Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
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140
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
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160

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