Papers with deeper language understanding
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension (P18-1)
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| Challenge: | DuoRC contains 186,089 unique question-answer pairs created from 7680 movie plots . |
| Approach: | They propose a novel dataset for Reading Comprehension that motivates new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
| Outcome: | The proposed dataset motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
Large-scale Cloze Test Dataset Created by Teachers (D18-1)
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| Challenge: | Existing cloze tests are used to evaluate language proficiency in language exams, but they are not yet available. |
| Approach: | They propose to create a large-scale human-created cloze test dataset CLOTH, which contains questions used in middle-school and high-school language exams. |
| Outcome: | The proposed dataset outperforms existing models and shows that humans outperformed existing models by a significant margin. |
When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
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| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
| Outcome: | The proposed methods increase models' reliance on hidden biases instead of learning robust features that help them solve a task. |