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[P] Comparing 7 Deep Dependency parsing models using Tensorflow

Trained on CONLL English Dependency, https://github.com/UniversalDependencies/UD_English-EWT. Train set to train, dev and test sets to test.

Stackpointer and Biaffine-attention originally from https://github.com/XuezheMax/NeuroNLP2 written in Pytorch.

Accuracy based on arc, types and root accuracies after 15 epochs only.

  1. Bidirectional RNN + CRF + Biaffine, arc accuracy 70.48%, types accuracy 65.18%, root accuracy 66.4%
  2. Bidirectional RNN + Bahdanau + CRF + Biaffine, arc accuracy 70.82%, types accuracy 65.33%, root accuracy 66.77%
  3. Bidirectional RNN + Luong + CRF + Biaffine, arc accuracy 71.22%, types accuracy 65.73%, root accuracy 67.23%
  4. BERT Base + CRF + Biaffine, arc accuracy 64.30%, types accuracy 62.89%, root accuracy 74.19%
  5. Bidirectional RNN + Biaffine Attention + Cross Entropy, arc accuracy 72.42%, types accuracy 63.53%, root accuracy 68.51%
  6. BERT Base + Biaffine Attention + Cross Entropy, arc accuracy 72.85%, types accuracy 67.11%, root accuracy 73.93%
  7. Bidirectional RNN + Stackpointer, arc accuracy 61.88%, types accuracy 48.20%, root accuracy 89.39%

Link to repository, https://github.com/huseinzol05/NLP-Models-Tensorflow#dependency-parser

Discussion

  1. Based on 15 epochs only.
  2. No dropout here, feel free to do it.
  3. BERT cannot implemented in Stackpointer model, stack pointer model required each decoder step.

submitted by /u/huseinzol05
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.