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Found 6 papers, 3 papers with code
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Which questions should I answer? Salience Prediction of Inquisitive Questions
QSALIENCE is instruction-tuned over our dataset of linguist-annotated salience scores of 1, 766 (context, question) pairs.
QUDEVAL: The Evaluation of Questions Under Discussion Discourse Parsing
Questions Under Discussion (QUD) is a versatile linguistic framework in which discourse progresses as continuously asking questions and answering them.
Elaborative Simplification as Implicit Questions Under Discussion
Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex sentences to simplified sentences using encoder-decoder models.
Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion
Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
