| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
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Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . |
| Approach: | They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension . |
| Outcome: | The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks . |
| Approach: | They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates . |
| Outcome: | The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories . |
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Explicit Span-Sentence Predication solves location unit ambiguity problem in many languages, allowing model to determine which sentence contains the answer span when sentence itself has not been clearly defined at all. |
| Approach: | They propose a machine-learning reader with Explicit Span-Sentence Predication to solve this problem by analyzing Chinese sentences. |
| Outcome: | The proposed reader achieves state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages. |
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. |
A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)
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| Challenge: | despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents. |
| Approach: | They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles . |
| Outcome: | The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. |
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)
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| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
| Approach: | They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs) |
| Outcome: | The QASE module surpasses state-of-the-art models in few-shot settings. |
Cross-Lingual Machine Reading Comprehension (D19-1)
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text (2020.emnlp-main)
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| Challenge: | MRC has achieved significant progress on the open domain in recent years due to large-scale pre-trained language models. |
| Approach: | They propose a machine reading comprehension model which exploits structural medical knowledge and reference medical plain text to improve the exam's accuracy. |
| Outcome: | The proposed model outperforms existing models with a large margin and passes the exam with 61.8% accuracy rate on the test set. |
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. |