Effective Self-Training for Parsing
We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1%, an absolute 1.1% improvement (12% error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon.
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Tasks
Datasets
Penn Treebank
Results from the Paper
Ranked #23 on
Constituency Parsing
on Penn Treebank
(using extra training data)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Constituency Parsing | Penn Treebank | Self-training | F1 score | 92.1 | # 23 |
