| Challenge: | Recent studies have shown that questions require a deeper understanding of language to answer beyond using superficial cues. |
| Approach: | They propose to use simple heuristics to split MRC datasets into easy and hard subsets and manually annotate questions from each subset with validity and reasoning skills to investigate which skills explain the difference between easy and harder questions. |
| Outcome: | The proposed model performs better for hard and easy questions than for easy questions. |
Similar Papers
What Makes Reading Comprehension Questions Difficult? (2022.acl-long)
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| Challenge: | a recent study shows that natural language understanding benchmarks are not able to measure future progress . a crowdsourcing approach is needed to collect diverse examples without sacrificing diversity or coverage. |
| Approach: | They crowdsource multiple-choice reading comprehension questions for passages from seven sources . they find passage source, length, and readability measures do not significantly affect question difficulty . |
| Outcome: | The results show that passage source, length, and readability measures do not significantly affect question difficulty. |
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. |
| Approach: | They propose to use shortcut questions to analyze learning difficulty of MRC models . they propose to analyze the learning difficulty regarding shortcut and challenging questions . |
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Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension (C18-1)
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| Challenge: | Existing MRC algorithms assume that each question is answerable by looking at text passages, but to realize human-like language comprehension ability, a machine should be able to distinguish not-answerable questions from answerable questions. |
| Approach: | They propose a method for automatically assigning difficulty level labels to a dataset that alters an existing MRC dataset and describes the resulting dataset. |
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English Machine Reading Comprehension Datasets: A Survey (2021.emnlp-main)
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| Challenge: | a survey of English Machine Reading Comprehension datasets is carried out . the aim is to provide a concise yet informative overview of the landscape . |
| Approach: | They survey 60 English Machine Reading Comprehension datasets to provide a resource for other researchers interested in this problem. |
| Outcome: | The proposed survey covers 60 English MRC datasets with a view to providing a resource for other researchers interested in the problem. |
On Making Reading Comprehension More Comprehensive (D19-58)
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| Challenge: | Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text. |
| Approach: | They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text. |
| Outcome: | The proposed questions have surface cues or other biases that allow a model to shortcut the intended reasoning process. |
GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation (2021.findings-acl)
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| Challenge: | Existing machine reading comprehension datasets lack an explainable evaluation of systems' reasoning capabilities. |
| Approach: | They propose a dataset with multi-choice questions that evaluates MRC systems' reasoning process . they use sentence-level relevant supporting facts, error reason of distractors to evaluate MRC . |
| Outcome: | The proposed dataset is more challenging and useful for identifying limitations of existing MRC systems in an explainable way. |
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
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| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
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What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
| Outcome: | The proposed models outperform human baselines on the widely-used SQuAD 1.1 and SQu AD 2.0 datasets. |
Coreference Reasoning in Machine Reading Comprehension (2021.acl-long)
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| Challenge: | Existing datasets for machine reading comprehension do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Approach: | They propose to use existing coreference resolution datasets to train machine reading comprehension models to better reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Outcome: | The proposed method improves the performance of state-of-the-art models on a set of coreference-related datasets. |
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method (2022.lrec-1)
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| Challenge: | Multiple-choice question answering (MCQA) requires a model to understand natural languages and understand textual representations. |
| Approach: | They propose a model that uses human reading comprehension attention to increase accuracy for machine reading comprehension. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Semeval-2018 Task 11 dataset and on the DREAM dataset. |