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 851900 of 1760 papers

TitleStatusHype
RVISA: Reasoning and Verification for Implicit Sentiment Analysis0
S2ST-Omni: An Efficient and Scalable Multilingual Speech-to-Speech Translation Framework via Seamless Speech-Text Alignment and Streaming Speech Generation0
Samajh-Boojh: A Reading Comprehension system in Hindi0
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling0
SaulLM-7B: A pioneering Large Language Model for Law0
SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis0
Scalable Neural Theorem Proving on Knowledge Bases and Natural Language0
Scene Restoring for Narrative Machine Reading Comprehension0
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension0
Scientific Discovery as Link Prediction in Influence and Citation Graphs0
SciMRC: Multi-perspective Scientific Machine Reading Comprehension0
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View0
Scoping natural language processing in Indonesian and Malay for education applications0
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension0
Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods0
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing0
Self-Supervised Test-Time Learning for Reading Comprehension0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification0
Semantic Framework for Comparison Structures in Natural Language0
Semantics-Aware Inferential Network for Natural Language Understanding0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management0
SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity0
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge0
Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations0
Semi-Supervised Clustering for Short Answer Scoring0
Semi-supervised Training Data Generation for Multilingual Question Answering0
Sense-Specific Lexical Information for Reading Assistance0
Sentence Complexity Estimation for Chinese-speaking Learners of Japanese0
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese0
Separating Answers from Queries for Neural Reading Comprehension0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation0
Sequential Attention: A Context-Aware Alignment Function for Machine Reading0
Set Expansion using Sibling Relations between Semantic Categories0
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task0
SG-Net: Syntax Guided Transformer for Language Representation0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension0
Short Answer Assessment: Establishing Links Between Research Strands0
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives0
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering0
Simplifying metaphorical language for young readers: A corpus study on news text0
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions0
Six Good Predictors of Autistic Text Comprehension0
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval0
SkillQG: Learning to Generate Question for Reading Comprehension Assessment0
Slot Filling for Biomedical Information Extraction0
Smarnet: Teaching Machines to Read and Comprehend Like Human0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension0
Social Bias in Popular Question-Answering Benchmarks0
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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