Machine Reading Comprehension
197 papers with code • 4 benchmarks • 41 datasets
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.
Benchmarks
These leaderboards are used to track progress in Machine Reading Comprehension
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Libraries
Use these libraries to find Machine Reading Comprehension models and implementations
MCTest
MRQA
CBT
ReClor
LogiQA
DREAM
CMRC
MuTual
DRCD
Quoref
Latest papers with no code
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change
By creating these baselines, researchers can more easily utilize Pir\'a as a resource for testing machine learning models across a wide range of question answering tasks.
Multi-turn Dialogue Comprehension from a Topic-aware Perspective
On the other hand, the split segments are an appropriate element of multi-turn dialogue response selection.
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
The objective of this study is to devise a strategy that can improve the model's capability to recognize biomedical entities in scenarios of few-shot learning.
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
This results in optimized attention between the two if a relationship exists.
Teach model to answer questions after comprehending the document
Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text.
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
However, the dataset has ignored the fact that different readers may have different levels of understanding of the text, and only includes single-perspective question-answer pairs, leading to a lack of consideration of different perspectives.
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Importantly, LEDO is computationally efficient compared to methods that require loss function change, and cost-effective as the resulting data can be used in the same continuous training pipeline for production.
Machine Reading Comprehension using Case-based Reasoning
Given a test question, CBR-MRC first retrieves a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers in the retrieved cases.
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models.
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP).
