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katsutan

katsutan
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Decks

What does BERT learn about the structure of language?
katsutan
0
160
Simple and Effective Paraphrastic Similarity from Parallel Translations
katsutan
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140
Simple task-specific bilingual word embeddings
katsutan
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150
Retrofitting Contextualized Word Embeddings with Paraphrases
katsutan
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170
Character Eyes: Seeing Language through Character-Level Taggers
katsutan
1
130
Improving Word Embeddings Using Kernel PCA
katsutan
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160
Better Word Embeddings by Disentangling Contextual n-Gram Information
katsutan
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180
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
katsutan
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190
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
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220
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
katsutan
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200
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
katsutan
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170
Unsupervised Statistical Machine Translation
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120
Named Entity Recognition With Parallel Recurrent Neural Networks
katsutan
2
160
Phrase-level Self-Attention Networks for Universal Sentence Encoding
katsutan
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170
How Transferable are Neural Networks in NLP Applications?
katsutan
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180
Split and Rephrase: Better Evaluation and a Stronger Baseline
katsutan
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130
Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
katsutan
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130
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
katsutan
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140

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