Papers with MRC benchmarks
ARES: A Reading Comprehension Ensembling Service (2020.emnlp-demos)
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Anthony Ferritto, Lin Pan, Rishav Chakravarti, Salim Roukos, Radu Florian, J. William Murdock, Avi Sil
| Challenge: | ARES is a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Approach: | They propose a machine reading comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. |
| Outcome: | The proposed system increases F1 by 2.3 points on a short answer task using an ensemble of models. |
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning (2023.findings-acl)
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| Challenge: | Existing systems for logical reasoning have surpassed the average performance of humans in many tasks like SQuAD but there is still a long way to go when it comes to logical reasoning. |
| Approach: | They propose an InDicator-Oriented Logic Pre-training task which logically strengthens pre-trained models with the help of 6 types of logical indicators and a logicalally rich dataset. |
| Outcome: | The proposed task achieves state-of-the-art on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC. |
Natural Response Generation for Chinese Reading Comprehension (2023.findings-emnlp)
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| Challenge: | MRC models trained on labeled answers are limited in generating human-like responses in real QA scenarios. |
| Approach: | They construct a dataset called Penguin to promote machine reading comprehension . they use 200k training data with fluent, well-informed responses to train models . |
| Outcome: | The proposed dataset is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale. |