BiaffineDependencyParsing
BERT+Transformer+Biaffine dependency parser
Update
[2020-04-23] 修复数据加载中一个bug，在use_cache=True时可大幅度缩短数据加载准备耗时
Result
以上结果均为在Semeval-2016-Test集上测试得到的LAS
详细结果见: metrics记录
Semeval-2016 Task9数据集
原始评测数据集：HIT-SCIR/SemEval-2016: SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
原始数据集格式不符合conllu格式，你也可以使用我们处理过后的数据集，或者使用脚本自己处理
数据集处理完成之后需要在yaml配置文件中写入，例如：
data_set:
data_dir: 'dataset/sem16'
train_file: 'train/text_news.train.conllu'
dev_file: 'dev/sdp_news_dev.conllu'
test_file: 'test/sdp_news_test.conllu'
同时根据数据集的相关信息合理设置per_gpu_train_batch_size、max_seq_len等参数
使用
训练Train
查看帮助信息：python main.py train -h
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py train -c config_files/bert_biaffine.yamltorch.distributed 分布式训练
distributedDataParallel VS dataParallel
当GPU卡数较多或数据量较大（或者max_seq_len较大时），推荐使用
torch.distributed训练
CUDA_VISIBLE_DEVICES=0,1,2,3,5,6 python -m torch.distributed.launch --nproc_per_node=6 main.py -c config_files/bert_biaffine.yaml验证Dev
查看帮助信息：python main.py dev -h
CUDA_VISIBLE_DEVICES=0 python main.py dev -m <模型保存路径> -i <输入gold conllu文件> -o <输出文件>推理Inference
查看帮助信息：python main.py infer -h
CUDA_VISIBLE_DEVICES=0 python main.py infer -m <模型保存路径> -i <输入conllu文件> -o <输出文件>References
[1] Ding, Y., Shao, Y., Che, W., Liu, T., 2014. Dependency Graph Based Chinese Semantic Parsing, in: Sun, M., Liu, Y., Zhao, J. (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 58–69. https://doi.org/10.1007/978-3-319-12277-9_6
[2] Wang, Y., Che, W., Guo, J., Liu, T., 2018. A Neural Transition-Based Approach for Semantic Dependency Graph Parsing, in: AAAI.
[3] Dozat, T., Manning, C.D., 2018. Simpler but More Accurate Semantic Dependency Parsing, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Presented at the ACL 2018, Association for Computational Linguistics, Melbourne, Australia, pp. 484–490. https://doi.org/10.18653/v1/P18-2077
[4] Li, Y., Li, Z., Zhang, M., Wang, R., Li, S., Si, L., 2019. Self-attentive biaffine dependency parsing, in: IJCAI International Joint Conference on Artificial Intelligence. pp. 5067–5073. https://doi.org/10.24963/ijcai.2019/704
[5] Kondratyuk, D., Straka, M., 2019. 75 Languages, 1 Model: Parsing Universal Dependencies Universally. pp. 2779–2795. https://doi.org/10.18653/v1/d19-1279