| Challenge: | a lack of intermediate supervision makes learning difficult for complex compositional reading comprehension datasets . lack of supervision makes the learning difficult, leading to the lack of correct latent reasoning steps. |
| Approach: | They propose to collect intermediate reasoning supervision along with the final answer during data collection . they find that this helps combat some of the biases introduced during the data collection process . |
| Outcome: | The use of intermediate reasoning supervision improves model performance for complex compositional datasets. |
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| Challenge: | Existing work on decompositions of complex questions has focused on multi-step reasoning . but, in machine reading, it is unclear when decomposing is helpful . |
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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020.acl-main)
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman
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| Challenge: | a new method to learn compositional structured models is needed . end-task supervision provides only a weak indirect signal on values the latent decisions should take. |
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Weakly- and Semi-supervised Evidence Extraction (2020.findings-emnlp)
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| Challenge: | Existing methods to combine evidence annotations with document labels are limited to a minority of training examples. |
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Partial Or Complete, That’s The Question (N19-1)
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Why Machine Reading Comprehension Models Learn Shortcuts? (2021.findings-acl)
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| Challenge: | Existing studies show that many MRC models learn shortcuts to outwit benchmarks, but the performance is unsatisfactory in real-world applications. |
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