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 15511600 of 1760 papers

TitleStatusHype
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
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
Improving Neural Knowledge Base Completion with Cross-Lingual Projections0
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension0
Resource-Lean Modeling of Coherence in Commonsense Stories0
Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?Code0
LSDSem 2017 Shared Task: The Story Cloze Test0
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering0
Linguistic Knowledge as Memory for Recurrent Neural Networks0
A Comparative Study of Word Embeddings for Reading Comprehension0
Structural Embedding of Syntactic Trees for Machine Comprehension0
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task0
Machine Reading with Background Knowledge0
Building Large Machine Reading-Comprehension Datasets using Paragraph VectorsCode0
Multi-Perspective Context Matching for Machine ComprehensionCode0
Reading Comprehension using Entity-based Memory Network0
QAF: Frame Semantics-based Question Interpretation0
Filling a Knowledge Graph with a Crowd0
SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning0
The Open Framework for Developing Knowledge Base And Question Answering System0
Textual complexity as a predictor of difficulty of listening items in language proficiency tests0
Dedicated Workflow Management for OKBQA Framework0
Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier0
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading0
Chinese Hypernym-Hyponym Extraction from User Generated Categories0
Korean FrameNet Expansion Based on Projection of Japanese FrameNet0
Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia0
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts0
Effect of Syntactic Features in Bangla Sentence Comprehension0
Emergent Predication Structure in Hidden State Vectors of Neural Readers0
Hierarchical Question Answering for Long Documents0
A Compare-Aggregate Model for Matching Text SequencesCode0
Bidirectional Attention Flow for Machine ComprehensionCode0
Learning Recurrent Span Representations for Extractive Question AnsweringCode0
An Analysis of Prerequisite Skills for Reading Comprehension0
Nested Propositions in Open Information Extraction0
Towards Broad-coverage Meaning Representation: The Case of Comparison Structures0
Porting an Open Information Extraction System from English to GermanCode0
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension0
Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources0
Broad Context Language Modeling as Reading Comprehension0
Learning to Reason With Adaptive Computation0
Reasoning with Memory Augmented Neural Networks for Language Comprehension0
Gated End-to-End Memory NetworksCode0
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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