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
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.
Leveraging External Knowledge Resources to Enable Domain-Specific Comprehension
Machine Reading Comprehension (MRC) has been a long-standing problem in NLP and, with the recent introduction of the BERT family of transformer based language models, it has come a long way to getting solved.
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference.
Multi-grained Evidence Inference for Multi-choice Reading Comprehension
Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options.
