research
∙
05/12/2022
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
Neural abstractive summarization models are prone to generate summaries ...
share
research
∙
09/09/2021
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Recent prompt-based approaches allow pretrained language models to achie...
research
∙
10/23/2020
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures
Existing NLP datasets contain various biases, and models tend to quickly...
research
∙
09/25/2020
Towards Debiasing NLU Models from Unknown Biases
NLU models often exploit biases to achieve high dataset-specific perform...
research
∙
05/01/2020
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Models for natural language understanding (NLU) tasks often rely on the ...
research
∙
09/19/2019