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katsutan
katsutan
42 Decks
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What does BERT learn about the structure of language?
katsutan
0
280
Simple and Effective Paraphrastic Similarity from Parallel Translations
katsutan
0
240
Simple task-specific bilingual word embeddings
katsutan
0
250
Retrofitting Contextualized Word Embeddings with Paraphrases
katsutan
0
300
Character Eyes: Seeing Language through Character-Level Taggers
katsutan
1
250
Improving Word Embeddings Using Kernel PCA
katsutan
0
250
Better Word Embeddings by Disentangling Contextual n-Gram Information
katsutan
0
370
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
katsutan
0
300
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
katsutan
0
320
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
katsutan
0
300
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
katsutan
0
250
Unsupervised Statistical Machine Translation
katsutan
0
190
Named Entity Recognition With Parallel Recurrent Neural Networks
katsutan
2
240
Phrase-level Self-Attention Networks for Universal Sentence Encoding
katsutan
0
280
How Transferable are Neural Networks in NLP Applications?
katsutan
0
280
Split and Rephrase: Better Evaluation and a Stronger Baseline
katsutan
0
190
Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
katsutan
0
170
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
katsutan
0
220
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