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 351400 of 1760 papers

TitleStatusHype
CliCR: A Dataset of Clinical Case Reports for Machine Reading ComprehensionCode0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
Asking Again and Again: Exploring LLM Robustness to Repeated QuestionsCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Fine-Grained Prediction of Reading Comprehension from Eye MovementsCode0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading ComprehensionCode0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Chunk, Align, Select: A Simple Long-sequence Processing Method for TransformersCode0
Cognitive Graph for Multi-Hop Reading Comprehension at ScaleCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case StudyCode0
Explaining Interactions Between Text SpansCode0
Generating Question-Answer HierarchiesCode0
Exploiting Explicit Paths for Multi-hop Reading ComprehensionCode0
Fast Reading Comprehension with ConvNetsCode0
ChID: A Large-scale Chinese IDiom Dataset for Cloze TestCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Commonsense for Generative Multi-Hop Question Answering TasksCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading ComprehensionCode0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
Implicit Argument Prediction as Reading ComprehensionCode0
Evaluating Large Language Models on Controlled Generation TasksCode0
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language ModelsCode0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?Code0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Compositional Questions Do Not Necessitate Multi-hop ReasoningCode0
Character Identification on Multiparty Conversation: Identifying Mentions of Characters in TV ShowsCode0
Medical device surveillance with electronic health recordsCode0
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge SourceCode0
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming DataCode0
Are you tough enough? Framework for Robustness Validation of Machine Comprehension SystemsCode0
Joint Learning of Sentence Embeddings for Relevance and EntailmentCode0
EQuANt (Enhanced Question Answer Network)Code0
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversationsCode0
Chaining Event Spans for Temporal Relation GroundingCode0
Are you tough enough? Framework for Robustness Validation of Machine Comprehension SystemsCode0
Entity Tracking Improves Cloze-style Reading ComprehensionCode0
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Estimating Linguistic Complexity for Science TextsCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
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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