Papers with noisy user-generated text
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling (2021.findings-acl)
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| Challenge: | Standard sequence labeling systems fail when processing noisy user-generated text or consuming the output of an OCR process. |
| Approach: | They propose an empirical error generation approach that employs a sequence-to-sequence model trained to perform translation from error-free to erroneous text. |
| Outcome: | The proposed method outperforms baseline noise generation and error correction techniques on the erroneous sequence labeling data sets. |
Robustness to Capitalization Errors in Named Entity Recognition (D19-55)
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| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |