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.
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Datasets
Results from the Paper
Ranked #1 on
2D Human Pose Estimation
on Alibaba Cluster Trace
(using extra training data)
| 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 |
