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 201250 of 1760 papers

TitleStatusHype
Beat the AI: Investigating Adversarial Human Annotation for Reading ComprehensionCode1
Break It Down: A Question Understanding BenchmarkCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
Retrospective Reader for Machine Reading ComprehensionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
Knowledge Guided Text Retrieval and Reading for Open Domain Question AnsweringCode1
Coreference Resolution as Query-based Span PredictionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State TrackingCode1
Revealing the Importance of Semantic Retrieval for Machine Reading at ScaleCode1
Interactive Language Learning by Question AnsweringCode1
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential ReasoningCode1
RoBERTa: A Robustly Optimized BERT Pretraining ApproachCode1
XQA: A Cross-lingual Open-domain Question Answering DatasetCode1
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
Zero-Shot Entity Linking by Reading Entity DescriptionsCode1
E3: Entailment-driven Extracting and Editing for Conversational Machine ReadingCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisCode1
Language Models are Unsupervised Multitask LearnersCode1
Densely Connected Attention Propagation for Reading ComprehensionCode1
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention NetworksCode1
Generating Distractors for Reading Comprehension Questions from Real ExaminationsCode1
Jack the Reader - A Machine Reading FrameworkCode1
Know What You Don't Know: Unanswerable Questions for SQuADCode1
QANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionCode1
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening ComprehensionCode1
AllenNLP: A Deep Semantic Natural Language Processing PlatformCode1
Simple and Effective Multi-Paragraph Reading ComprehensionCode1
Zero-Shot Relation Extraction via Reading ComprehensionCode1
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading ComprehensionCode1
Learning to Ask: Neural Question Generation for Reading ComprehensionCode1
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion AnnotationCode1
Reading Wikipedia to Answer Open-Domain QuestionsCode1
Making Neural QA as Simple as Possible but not SimplerCode1
NewsQA: A Machine Comprehension DatasetCode1
MS MARCO: A Human Generated MAchine Reading COmprehension DatasetCode1
Words or Characters? Fine-grained Gating for Reading ComprehensionCode1
SQuAD: 100,000+ Questions for Machine Comprehension of TextCode1
Listen, Attend and SpellCode1
Teaching Machines to Read and ComprehendCode1
Towards AI-Complete Question Answering: A Set of Prerequisite Toy TasksCode1
Chaining Event Spans for Temporal Relation GroundingCode0
S2ST-Omni: An Efficient and Scalable Multilingual Speech-to-Speech Translation Framework via Seamless Speech-Text Alignment and Streaming Speech Generation0
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversationsCode0
Automatic Generation of Inference Making Questions for Reading Comprehension AssessmentsCode0
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View0
Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
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