Papers by Steven Cao
Multilingual Constituency Parsing with Self-Attention and Pre-Training (P19-1)
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| Challenge: | a range of pre-training conditions can be used for constituency parsing, but large model sizes make it expensive to train separate models for each language. |
| Approach: | They compare the benefits of no pre-training, fastText, ELMo, and BERT for English . they also find that pre- training is beneficial across all 11 languages tested . |
| Outcome: | The proposed model outperforms fastText, ELMo, and BERT for English . but large model sizes make it expensive to train separate models for each language . |
Low-Complexity Probing via Finding Subnetworks (2021.naacl-main)
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| Challenge: | Existing approaches to probing neural networks for linguistic properties are to train a shallow multi-layer perceptron (MLP) on top of the model's internal representations. |
| Approach: | They propose a subtractive pruning-based probe where they find an existing subnetwork that performs the linguistic task of interest. |
| Outcome: | The proposed probe achieves higher accuracy on pre-trained models and lower accuracy on random models, and better learning on its own. |
Unsupervised Parsing via Constituency Tests (2020.emnlp-main)
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| Challenge: | Existing methods for unsupervised parsing rely on constituency tests . linguists can judge a sentence's grammatical validity by modifying it via some transformation . |
| Approach: | They propose a method for unsupervised parsing based on a constituency test . they specify a set of transformations and use an unsupervised neural acceptability model to make grammaticality decisions. |
| Outcome: | The proposed method achieves 62.8 F1 on the Penn Treebank test set, an improvement of 7.6 points over the previous best results. |