About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
COLING 2022
Conference paper
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL
Abstract
Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. Models trained on Spider dataset, despite achieving 75% accuracy on Spider development or test sets, show a huge decline below 20% accuracy for databases not in Spider. We present a system that combines automated training-data augmentation and ensemble technique. We achieve double-digit percentage improvement for databases that are not part of the Spider corpus.