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 601650 of 1760 papers

TitleStatusHype
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
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced LanguagesCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Learning Graph Representation of Agent DiffusersCode0
Document Modeling with External Attention for Sentence ExtractionCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case StudyCode0
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?Code0
Learning to Search in Long Documents Using Document StructureCode0
AllenNLP Interpret: A Framework for Explaining Predictions of NLP ModelsCode0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Exploiting Explicit Paths for Multi-hop Reading ComprehensionCode0
BERT Based Multilingual Machine Comprehension in English and HindiCode0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Evaluating Large Language Models on Controlled Generation TasksCode0
Adaptive loose optimization for robust question answeringCode0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
Gated-Attention Readers for Text ComprehensionCode0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
Repartitioning of the ComplexWebQuestions DatasetCode0
Do Chinese models speak Chinese languages?0
Benefits of Intermediate Annotations in Reading Comprehension0
Annotating the MASC Corpus with BabelNet0
Distributed Vector Representations for Unsupervised Automatic Short Answer Grading0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach0
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change0
Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment0
Discrete Reasoning Templates for Natural Language Understanding0
Annotating Story Timelines as Temporal Dependency Structures0
AgentInstruct: Toward Generative Teaching with Agentic Flows0
Discourse-Wide Extraction of Assay Frames from the Biological Literature0
Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
DIFM:An effective deep interaction and fusion model for sentence matching0
Difficulty Controllable Generation of Reading Comprehension Questions0
Benben: A Chinese Intelligent Conversational Robot0
Annotating Entailment Relations for Shortanswer Questions0
A Framework for Rationale Extraction for Deep QA models0
Dialog State Tracking: A Neural Reading Comprehension Approach0
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature0
Dialog state tracking, a machine reading approach using Memory Network0
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension0
Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes0
An NLP-based Reading Tool for Aiding Non-native English Readers0
A Framework for Learning Assessment through Multimodal Analysis of Reading Behaviour and Language Comprehension0
Show:102550
← PrevPage 13 of 36Next →

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