Papers by Shanbo Chu
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks (2020.emnlp-main)
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| Challenge: | Neural networks typically need large labeled data for training and are not easily interpretable. |
| Approach: | They propose a type of recurrent neural networks that combine neural networks and regular expression rules. |
| Outcome: | The proposed recurrent neural networks outperform previous neural approaches in low- and zero-shot scenarios and remain very competitive in rich-resource settings. |
Learning Numeral Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embedding methods do not learn numeral embedds well because numerals are limited in number and their appearances in training corpora are highly scarce. |
| Approach: | They propose two numeral embedding methods that can handle the out-of-vocabulary problem for numerals. |
| Outcome: | The proposed methods can handle the out-of-vocabulary problem for numerals. |