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 201–250 of 1760 papers

TitleStatusHype
NarrativeXL: A Large-scale Dataset For Long-Term Memory ModelsCode1
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
Break It Down: A Question Understanding BenchmarkCode1
NorQuAD: Norwegian Question Answering DatasetCode1
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Open-Domain Question Answering Goes Conversational via Question RewritingCode1
PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel ComputationCode1
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention NetworksCode1
ParsiNLU: A Suite of Language Understanding Challenges for PersianCode1
PMG : Personalized Multimodal Generation with Large Language ModelsCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
A Unified MRC Framework for Named Entity RecognitionCode1
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Adversarial Training for Commonsense InferenceCode1
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion AnnotationCode1
Reasoning in Dialog: Improving Response Generation by Context Reading ComprehensionCode1
Automated Scoring for Reading Comprehension via In-context BERT TuningCode1
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
Relation-aware Video Reading Comprehension for Temporal Language GroundingCode1
Retrospective Reader for Machine Reading ComprehensionCode1
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Benchmarking: Past, Present and FutureCode1
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading ComprehensionCode1
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language ModelsCode1
Simple and Effective Multi-Paragraph Reading ComprehensionCode1
Spans, Not Tokens: A Span-Centric Model for Multi-Span Reading ComprehensionCode1
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening ComprehensionCode1
Can large language models reason about medical questions?Code1
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about NegationCode1
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
The neural architecture of language: Integrative modeling converges on predictive processingCode1
TIE: Topological Information Enhanced Structural Reading Comprehension on Web PagesCode1
Towards AI-Complete Question Answering: A Set of Prerequisite Toy TasksCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning SkillsCode1
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
Towards artificial general intelligence via a multimodal foundation modelCode1
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language ModelsCode1
Annotating the MASC Corpus with BabelNet—0
Annotating Story Timelines as Temporal Dependency Structures—0
AgentInstruct: Toward Generative Teaching with Agentic Flows—0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications—0
A Framework for Rationale Extraction for Deep QA models—0
Benben: A Chinese Intelligent Conversational Robot—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—Unverified
5Knowledge modelTest79.2—Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2—Unverified
7LReasoner ensembleTest76.1—Unverified
8ELECTRA and ALBERTTest71—Unverified
9WWZTest69.7—Unverified
10xlnet-large-uncased [extended data]Test69.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4—Unverified
2Megatron-BERT (ensemble)Accuracy90.9—Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8—Unverified
4Megatron-BERTAccuracy89.5—Unverified
5XLNetAccuracy (Middle)88.6—Unverified
6DeBERTalargeAccuracy86.8—Unverified
7B10-10-10Accuracy85.7—Unverified
8RoBERTaAccuracy83.2—Unverified
9Orca 2-13BAccuracy82.87—Unverified
10Orca 2-7BAccuracy80.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94—Unverified
2MT5 LargeAverage F10.84—Unverified
3ruRoberta-large finetuneAverage F10.83—Unverified
4ruT5-large-finetuneAverage F10.82—Unverified
5Human BenchmarkAverage F10.81—Unverified
6ruT5-base-finetuneAverage F10.77—Unverified
7ruBert-large finetuneAverage F10.76—Unverified
8ruBert-base finetuneAverage F10.74—Unverified
9RuGPT3XL few-shotAverage F10.74—Unverified
10RuGPT3LargeAverage F10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4—Unverified
2BERT-LargeOverall: F162.7—Unverified
3BiDAFOverall: F128.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55—Unverified