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 16011650 of 1760 papers

TitleStatusHype
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions0
Gated Self-Matching Networks for Reading Comprehension and Question Answering0
Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning0
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution0
Generating Diagnostic Multiple Choice Comprehension Cloze Questions0
Generating Feedback for English Foreign Language Exercises0
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts0
Generating Questions for Reading Comprehension using Coherence Relations0
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model0
GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children0
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval0
Getting the Most out of AMR Parsing0
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension0
Global memory transformer for processing long documents0
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker0
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM0
Text Understanding in GPT-4 vs Humans0
Graph-Based Knowledge Integration for Question Answering over Dialogue0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model0
Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy0
Graphical Schemes May Improve Readability but Not Understandability for People with Dyslexia0
Graph Sequential Network for Reasoning over Sequences0
Grounding Gradable Adjectives through Crowdsourcing0
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering0
Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human Literacy and Education Benchmarks0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension0
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension0
HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French0
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation0
Hierarchical Learning for Generation with Long Source Sequences0
Hierarchical Question Answering for Long Documents0
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models0
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs0
How Context Affects Language Models' Factual Predictions0
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks0
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?0
How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method0
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data0
IBERT: Idiom Cloze-style reading comprehension with Attention0
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications0
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation0
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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