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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 381–390 of 555 papers

TitleStatusHype
Teaching Machine Comprehension with Compositional ExplanationsCode1
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension—0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension—0
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora—0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts—0
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions—0
Knowledgeable Dialogue Reading Comprehension on Key Turns—0
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