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 15011550 of 1760 papers

TitleStatusHype
Evaluation of Automatically Generated Pronoun Reference Questions0
Simplifying metaphorical language for young readers: A corpus study on news text0
Splitting Complex English Sentences0
Investigating neural architectures for short answer scoring0
Continuous fluency tracking and the challenges of varying text complexity0
Question Generation for Question Answering0
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions0
A Question Answering Approach for Emotion Cause Extraction0
Story Comprehension for Predicting What Happens Next0
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension0
Getting the Most out of AMR Parsing0
Learning what to read: Focused machine reading0
R^3: Reinforced Reader-Ranker for Open-Domain Question AnsweringCode0
A Question Answering Approach to Emotion Cause Extraction0
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator0
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension0
Question Dependent Recurrent Entity Network for Question AnsweringCode0
Adversarial Examples for Evaluating Reading Comprehension SystemsCode0
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension0
Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph0
Benben: A Chinese Intelligent Conversational Robot0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability0
Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task0
Coarse-to-Fine Question Answering for Long Documents0
Gated Self-Matching Networks for Reading Comprehension and Question Answering0
A Constituent-Centric Neural Architecture for Reading Comprehension0
Two-Stage Synthesis Networks for Transfer Learning in Machine ComprehensionCode0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension0
Neural Models for Key Phrase Detection and Question Generation0
Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models0
Zero-Shot Relation Extraction via Reading ComprehensionCode1
A Joint Model for Question Answering and Question Generation0
Learning to Compute Word Embeddings On the Fly0
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading ComprehensionCode1
Reinforced Mnemonic Reader for Machine Reading ComprehensionCode0
Sequential Attention: A Context-Aware Alignment Function for Machine Reading0
Machine Comprehension by Text-to-Text Neural Question GenerationCode0
Learning to Ask: Neural Question Generation for Reading ComprehensionCode1
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning0
Ruminating Reader: Reasoning with Gated Multi-Hop Attention0
SearchQA: A New Q&A Dataset Augmented with Context from a Search EngineCode0
RACE: Large-scale ReAding Comprehension Dataset From ExaminationsCode0
Automatic Classification of the Complexity of Nonfiction Texts in Portuguese for Early School Years0
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion AnnotationCode1
LSDSem 2017 Shared Task: The Story Cloze Test0
Show:102550
← PrevPage 31 of 36Next →

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