SOTAVerified

Reading Comprehension

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.

Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.

The Machine Reading group at UCL also provides an overview of reading comprehension tasks.

Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets

Papers

Showing 251300 of 1760 papers

TitleStatusHype
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rankCode0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Implicit Argument Prediction as Reading ComprehensionCode0
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question AnsweringCode0
Improving Machine Reading Comprehension with General Reading StrategiesCode0
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language ModelsCode0
A Framework for Evaluation of Machine Reading Comprehension Gold StandardsCode0
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
How to Engage Your Readers? Generating Guiding Questions to Promote Active ReadingCode0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?Code0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language ModelsCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
An Information-Theoretic Approach to Analyze NLP Classification TasksCode0
Automatic Opinion Question GenerationCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
GMAT: Global Memory Augmentation for TransformersCode0
A Nil-Aware Answer Extraction Framework for Question AnsweringCode0
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut TriggersCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
Generating Question-Answer HierarchiesCode0
Automatic Generation of Inference Making Questions for Reading Comprehension AssessmentsCode0
Gated-Attention Readers for Text ComprehensionCode0
Gated End-to-End Memory NetworksCode0
GENIE: Toward Reproducible and Standardized Human Evaluation for Text GenerationCode0
Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language ModelsCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
FusionNet: Fusing via Fully-Aware Attention with Application to Machine ComprehensionCode0
Interpreting Themes from Educational StoriesCode0
Fine-Grained Prediction of Reading Comprehension from Eye MovementsCode0
Automatically generating question-answer pairs for assessing basic reading comprehension in SwedishCode0
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine ComprehensionCode0
Fast Reading Comprehension with ConvNetsCode0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERTCode0
Automated Focused Feedback Generation for Scientific Writing AssistanceCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM ExtractionCode0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced LanguagesCode0
Augmenting Neural Networks with First-order LogicCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Exploiting Explicit Paths for Multi-hop Reading ComprehensionCode0
Adversarial Examples for Evaluating Reading Comprehension SystemsCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
5Knowledge modelTest79.2Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2Unverified
7LReasoner ensembleTest76.1Unverified
8ELECTRA and ALBERTTest71Unverified
9WWZTest69.7Unverified
10xlnet-large-uncased [extended data]Test69.3Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4Unverified
2Megatron-BERT (ensemble)Accuracy90.9Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8Unverified
4Megatron-BERTAccuracy89.5Unverified
5XLNetAccuracy (Middle)88.6Unverified
6DeBERTalargeAccuracy86.8Unverified
7B10-10-10Accuracy85.7Unverified
8RoBERTaAccuracy83.2Unverified
9Orca 2-13BAccuracy82.87Unverified
10Orca 2-7BAccuracy80.79Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94Unverified
2MT5 LargeAverage F10.84Unverified
3ruRoberta-large finetuneAverage F10.83Unverified
4ruT5-large-finetuneAverage F10.82Unverified
5Human BenchmarkAverage F10.81Unverified
6ruT5-base-finetuneAverage F10.77Unverified
7ruBert-large finetuneAverage F10.76Unverified
8ruBert-base finetuneAverage F10.74Unverified
9RuGPT3XL few-shotAverage F10.74Unverified
10RuGPT3LargeAverage F10.73Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4Unverified
2BERT-LargeOverall: F162.7Unverified
3BiDAFOverall: F128.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55Unverified