The code of our proposed GCDT, which deepens the state transition path at each position in a sentence, and further assign every token with a global representation learned from the entire sentence. [paper]. The implementation is based on THUMT.
- Trim Glove
sh trim_glove.sh path_to_glove
path_to_glove is the path of your decompressed Glove embedding.
- Training
sh train.sh task_name
task_name is the name of tasks between ner and chunking.
- Evaluation and Testing
sh test.sh task_name test_type
Set test_type to testa for evaluation and testb for testing.
Please note there is no evaluation set for the chunking task.
- tensorflow 1.12
- python 3.5
Please cite the following paper if you use the code:
@InProceedings{Liu:19,
author = {Yijin Liu, Fandong Meng, Jinchao Zhang, Jinan Xu, Yufeng Chen and Jie Zhou},
title = {GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling},
booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
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Why not evaluate along with training?
For training efficiency, we firstly train a model for specified steps, and restore checkpoints for evaluation and testing. For the CoNLL03, we compute the score on the test set at the best-performing checkpoints on the evaluation set. For the CoNLL2000, we compute the score on the test set directly.
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How to get BERT embeddings?
We provide a simple tool to gennerate the BERT embedding for sequence labeling tasks. And then assign
bert_emb_pathwith correct path and setuse_bertto True intrain.sh.
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