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Found 5 papers, 3 papers with code
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification
Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions.
Partial-input baselines show that NLI models can ignore context, but they don't
When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations.
Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions.
How often are errors in natural language reasoning due to paraphrastic variability?
We propose a metric for evaluating the paraphrastic consistency of natural language reasoning models based on the probability of a model achieving the same correctness on two paraphrases of the same problem.
Partial-input baselines show that NLI models can ignore context, but they don’t.
When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations.
