Papers with weakly supervised model
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
| Approach: | They propose a weakly supervised model that requires only the final answer as supervision to solve math word problems. |
| Outcome: | The proposed model achieves accuracy gains of 4.5% and 32% over current weakly-supervised methods on standard Math23K and AllArith datasets. |
Weakly Supervised Attention Networks for Entity Recognition (D19-1)
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| Challenge: | Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain. |
| Approach: | They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels. |
| Outcome: | The proposed model performs reasonably well on the task of entity recognition despite not having access to token-level ground truth data. |