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 551600 of 1760 papers

TitleStatusHype
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity LinkingCode0
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced LanguagesCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
A Causal View of Entity Bias in (Large) Language ModelsCode0
Effective Subword Segmentation for Text ComprehensionCode0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Exploiting Explicit Paths for Multi-hop Reading ComprehensionCode0
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsCode0
Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue GenerationCode0
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading ComprehensionCode0
Explaining Interactions Between Text SpansCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Bidirectional Attention for SQL GenerationCode0
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice QuestionsCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
Bidirectional Attention Flow for Machine ComprehensionCode0
Have my arguments been replied to? Argument Pair Extraction as Machine Reading ComprehensionCode0
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language ModelsCode0
Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense LabelingCode0
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading ComprehensionCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
FastFusionNet: New State-of-the-Art for DAWNBench SQuADCode0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
End-to-End Open-Domain Question Answering with BERTseriniCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Biased or Flawed? Mitigating Stereotypes in Generative Language Models by Addressing Task-Specific FlawsCode0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question AnsweringCode0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for BulgarianCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
Do Language Models Learn about Legal Entity Types during Pretraining?Code0
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?Code0
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position SelectorCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Evaluating Large Language Models on Controlled Generation TasksCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Entity Tracking Improves Cloze-style Reading ComprehensionCode0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
Document Modeling with External Attention for Sentence ExtractionCode0
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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