Papers with reading comprehension systems
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
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| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization (D19-1)
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| Challenge: | Existing models are not good at distinguishing distractor sentences which look related but do not answer the question. |
| Approach: | They propose a method to regularize question answering models by maximizing mutual information among passages, questions, and answers. |
| Outcome: | The proposed model achieves state-of-the-art on the Adversarial-SQuAD dataset. |
Single-dataset Experts for Multi-dataset Question Answering (2021.emnlp-main)
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| Challenge: | Prior work has focused on training one network on multiple datasets to build a model that performs well on all of the training datasets and generalizes and transfers better to new datasets. |
| Approach: | They combine multiple reading comprehension datasets to build a multi-dataset question answering model with an ensemble of single-data set experts. |
| Outcome: | The proposed model outperforms baseline models in in-distribution accuracy and generalization and transfer performance. |