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Found 8 papers, 3 papers with code
Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap
Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question–context lexical overlap.
Which Shortcut Solution Do Question Answering Models Prefer to Learn?
We assume that the learnability of shortcuts, i. e., how easy it is to learn a shortcut, is useful to mitigate the problem.
Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering
Contrary to our expectations, although models become sensitive to the four types of perturbations, we find that the OOD generalization is not improved.
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering
Specifically, we find that when the relative positions in a training set are biased, the performance on examples with relative positions unseen during training is significantly degraded.
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following
An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to interact with objects in 3D environments.
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap
Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question-context lexical overlap.
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation
While most existing QAG methods aim to improve the quality of synthetic examples, we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness.
Multi-style Generative Reading Comprehension
Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved.
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