Papers by Ananth Gottumukkala
Comprehensive Multi-Dataset Evaluation of Reading Comprehension (D19-58)
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| Challenge: | Recent research aims to facilitate training and evaluation on several reading comprehension datasets at the same time. |
| Approach: | They propose an evaluation server that reports performance on seven diverse reading comprehension datasets and includes synthetic augmentations to test models' ability to handle out-of-domain questions. |
| Outcome: | The evaluation server performs on seven reading comprehension datasets, and collects and includes synthetic augmentations for these datasets to test models' ability to handle out-of-domain questions. |
Dynamic Sampling Strategies for Multi-Task Reading Comprehension (2020.acl-main)
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| Challenge: | Prior work focused on model architecture or generalization to held out datasets and largely passed over the particulars of the multi-task learning set up. |
| Approach: | They propose a dynamic sampling strategy that selects instances proportional to the model's current performance on a dataset relative to its single task performance. |
| Outcome: | The proposed model outperforms the best model on ORB, a recent multitask reading comprehension benchmark. |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |