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 14761500 of 1760 papers

TitleStatusHype
Bidirectional Attention for SQL GenerationCode0
The NarrativeQA Reading Comprehension ChallengeCode0
Attentive Memory Networks: Efficient Machine Reading for Conversational SearchCode0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Contextualized Word Representations for Reading ComprehensionCode0
FusionNet: Fusing via Fully-Aware Attention with Application to Machine ComprehensionCode0
Dynamic Fusion Networks for Machine Reading Comprehension0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Fast Reading Comprehension with ConvNetsCode0
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension0
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks0
Multi-Mention Learning for Reading Comprehension with Neural Cascades0
Sentence Complexity Estimation for Chinese-speaking Learners of Japanese0
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L20
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification0
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation0
Simple and Effective Multi-Paragraph Reading ComprehensionCode1
Phase Conductor on Multi-layered Attentions for Machine Comprehension0
Constructing Datasets for Multi-hop Reading Comprehension Across Documents0
Smarnet: Teaching Machines to Read and Comprehend Like Human0
A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
Discourse-Wide Extraction of Assay Frames from the Biological Literature0
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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