• • conll2003
• • atis

• • CharLSTM+WordLSTM+CRF: Lample .etc, NAACL16
  • • Make a CoNLL-2003 batcher
  • • Implement trainer
  • • Implement WordLSTM + softmax
  • • Implement CharLSTM + WordLSTM + softmax
  • • Implement CharLSTM + WordLSTM + CRF
• • Tranformer encoder + CRF
• • BERT encoder + CRF
• • pytorch JIT compilable Viterbi Decoder https://github.com/atulkum/sequence_prediction/blob/master/NER_BERT/decoder.py#L9

• • Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
  • • Make a ATIS batcher
  • • Implement trainer
  • • Implement slot filler
  • • Implement intent

• • STRUCTVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

conda install pytorch -c pytorch

CoNLL-2003 can be downloaded from https://www.clips.uantwerpen.be/conll2003/ner/

ATIS dataset can be downloaded from split 0 split 1 split 2 split 3 split 4