Machine Reading Comprehension
197 papers with code • 4 benchmarks • 42 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
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs.
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension
Machine Reading Comprehension (MRC) poses a significant challenge in the field of Natural Language Processing (NLP).
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question.
Can a Multichoice Dataset be Repurposed for Extractive Question Answering?
The rapid evolution of Natural Language Processing (NLP) has favored major languages such as English, leaving a significant gap for many others due to limited resources.
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles.
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information
Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical Question Answering Systems (QAS) and transforming the landscape of accessing and applying medical information.
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0
We conclude that the BERT base model will be improved by incorporating the features.
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module.
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
This imbalance leads to misclassifications of the entity classes as the O-class.
