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 10011050 of 1760 papers

TitleStatusHype
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
STARC: Structured Annotations for Reading ComprehensionCode1
Bilingual Text Extraction as Reading Comprehension0
Knowledgeable Dialogue Reading Comprehension on Key Turns0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension0
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and SuggestionsCode0
Semantics-Aware Inferential Network for Natural Language Understanding0
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training0
Contextualized Representations Using Textual Encyclopedic Knowledge0
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World ApplicationsCode0
Answer Generation through Unified Memories over Multiple Passages0
Logic-Guided Data Augmentation and Regularization for Consistent Question AnsweringCode1
Exploring Probabilistic Soft Logic as a framework for integrating top-down and bottom-up processing of language in a task context0
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension0
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
What do Models Learn from Question Answering Datasets?Code1
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair GenerationCode0
"You are grounded!": Latent Name Artifacts in Pre-trained Language ModelsCode0
Evaluating Models' Local Decision Boundaries via Contrast SetsCode1
Benchmarking Machine Reading Comprehension: A Psychological Perspective0
Graph Sequential Network for Reasoning over Sequences0
R3: A Reading Comprehension Benchmark Requiring Reasoning Processes0
Procedural Reading Comprehension with Attribute-Aware Context Flow0
TREC CAsT 2019: The Conversational Assistance Track OverviewCode1
TextCaps: a Dataset for Image Captioning with Reading Comprehension0
A Framework for Evaluation of Machine Reading Comprehension Gold StandardsCode0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model0
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language ProcessingCode2
Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension0
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature0
Incorporating BERT into Neural Machine TranslationCode1
Undersensitivity in Neural Reading Comprehension0
FQuAD: French Question Answering Dataset0
ReClor: A Reading Comprehension Dataset Requiring Logical ReasoningCode1
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker0
Beat the AI: Investigating Adversarial Human Annotation for Reading ComprehensionCode1
Break It Down: A Question Understanding BenchmarkCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Retrospective Reader for Machine Reading ComprehensionCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
A Study of the Tasks and Models in Machine Reading Comprehension0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts0
A Survey on Machine Reading Comprehension Systems0
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model0
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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