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Found 7 papers, 4 papers with code
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples.
Abstractive Text Summarization
Natural Language Inference
+1
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures
Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective so that models learn less from biased examples.
Towards Debiasing NLU Models from Unknown Biases
Recently proposed debiasing methods are shown to be effective in mitigating this tendency.
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.
Improving Generalization by Incorporating Coverage in Natural Language Inference
Finally, we show that using the coverage information is not only beneficial for improving the performance across different datasets of the same task.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice.
