Stanford cs224n course assignments

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• assignment 1:
  Exploring word vectors (sparse or dense word representations).

• assignment 2:
  Implement Word2Vec with NumPy.

• assignment 3:
  Implement a neural transition-based dependency parser with PyTorch. (ref: A Fast and Accurate Dependency Parser using Neural Networks ( https://nlp.stanford.edu/pubs/emnlp2014-depparser.pdf))

• assignment 4:
  Implement neural machine translation (NMT) using a attentive encoder-decoder structure with LSTMs. (ref: Neural Machine Translation by Jointly Learning to Align and Translate (https://arxiv.org/abs/1409.0473))

• assignment 5:
  Improve NMT in assignment 4 by adding character-based encoder and decoder modules. (ref: Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models (https://arxiv.org/abs/1604.00788))