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Deep neural models for core NLP tasks based on Pytorch(version 2)

This is the code we used in the following papers

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Xuezhe Ma, Eduard Hovy

ACL 2016

Neural Probabilistic Model for Non-projective MST Parsing

Xuezhe Ma, Eduard Hovy


Stack-Pointer Networks for Dependency Parsing

Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig and Eduard Hovy

ACL 2018

It also includes the re-implementation of the Stanford Deep BiAffine Parser:

Deep Biaffine Attention for Neural Dependency Parsing

Timothy Dozat, Christopher D. Manning

ICLR 2017


  1. Upgraded the code to support PyTorch 1.3 and Python 3.6
  2. Re-factored code to better organization
  3. Implemented the batch version of Stack-Pointer Parser decoding algorithm, about 50 times faster!


Python 3.6, PyTorch >=1.3.1, Gensim >= 0.12.0

Data format

For the data format used in our implementation, please read this issue.

Running the experiments

First to the experiments folder:

cd experiments

Sequence labeling

To train a CRF POS tagger of PTB WSJ corpus,


where the arguments for train/dev/test data, together with the pretrained word embedding should be setup.

To train a NER model on CoNLL-2003 English data set,


Dependency Parsing

To train a Stack-Pointer parser, simply run


Remeber to setup the paths for data and embeddings.

To train a Deep BiAffine parser, simply run


Again, remember to setup the paths for data and embeddings.

To train a Neural MST parser,

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