Papers with error detection model
Detecting Annotation Errors in Morphological Data with the Transformer (2022.acl-short)
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| Challenge: | Annotation errors that stem from various sources are usually unavoidable when performing large-scale annotation of linguistic data. |
| Approach: | They evaluate the feasibility of using a deep learning model to detect annotator errors in morphological data sets that contain inflected word forms. |
| Outcome: | The proposed model detects typographic errors, linguistic confusion errors and self-adversarial errors on four languages. |
Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)
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| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
| Approach: | They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries . |
| Outcome: | The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown . |