SC-LSTM

Shared Cell LSTM

This repository contains the code used for multi-task sequence labeling experiments in SC-LSTM: Learning Task-Specific Representation in Multi-task Learning for Sequence Labeling paper, partly forked from the OpenNMT-py. If you use this code or our results in your research, we'd appreciate if you cite our apper as following:

@inproceedings{lu2019sc,
  title={SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling},
  author={Lu, Peng and Bai, Ting and Langlais, Philippe},
  booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  pages={2396--2406},
  year={2019}
}

Requirements

Python 3.5+, PyTorch 0.4 and Allennlp (For ELMo embeddings) are required for the current repo.

Steps

1. Preprossing data: NER {CoNLL2003}, Chunking {CoNLL2000} and POS {UD English POS} The files should formatted as same as the CoNLL 2000 dataset. For example,

   
   Rockwell NNP B-NP
   International NNP I-NP
   Corp. NNP I-NP
   's POS B-NP
   Tulsa NNP I-NP
   unit NN I-NP
   said VBD B-VP
   it PRP B-NP
   signed VBD B-VP
   a DT B-NP
   tentative JJ I-NP
   agreement NN I-NP
   extending VBG B-VP
   its PRP$ B-NP
   contract NN I-NP
   with IN B-PP
   Boeing NNP B-NP
   Co. NNP I-NP
   

   Then put CoNLL2003/CoNLL2000/Unidep datasets in ./data/ , put embedding files like glove.6B.100d.txt in ./data/emb You can change training groups by delete specific tasks in the dict:datasets_config of file config_uni.py

2. Train the model: command: python main_uni.py The default setting is in config_uni.py file.