Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task

This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED)... The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics. read more

PDF Abstract IJCNLP 2017 PDF IJCNLP 2017 Abstract
No code implementations yet. Submit your code now

Results from the Paper


 Ranked #1 on 2D Human Pose Estimation on Alibaba Cluster Trace (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
2D Human Pose Estimation Alibaba Cluster Trace mitsimpo 10-20% Mask PSNR 12 # 1

Methods


No methods listed for this paper. Add relevant methods here