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

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Showing 1501–1550 of 1760 papers

TitleStatusHype
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia—0
Effective Character-augmented Word Embedding for Machine Reading Comprehension—0
Effective Feature Integration for Automated Short Answer Scoring—0
Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier—0
Effect of Syntactic Features in Bangla Sentence Comprehension—0
Efficient LLM Inference with Kcache—0
EFLLex: A Graded Lexical Resource for Learners of English as a Foreign Language—0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge—0
Embracing data abundance: BookTest Dataset for Reading Comprehension—0
Emergent: a novel data-set for stance classification—0
Emergent Predication Structure in Hidden State Vectors of Neural Readers—0
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks—0
Empirical Methods for the Study of Denotation in Nominalizations in Spanish—0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information—0
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension—0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training—0
Enhanced Electronic Health Records Text Summarization Using Large Language Models—0
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension—0
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering—0
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations—0
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
Ensemble approach for natural language question answering problem—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis—0
Entity Linking for Tweets—0
Entity Linking meets Word Sense Disambiguation: a Unified Approach—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations—0
Evaluating a How-to Tip Machine Comprehension Model with QA Examples collected from a Community QA Site—0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples—0
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting—0
Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation—0
Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?—0
Evaluating Machine Reading Systems through Comprehension Tests—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Evaluating NLP Models via Contrast Sets—0
Evaluating the Meaning of Answers to Reading Comprehension Questions: A Semantics-Based Approach—0
Evaluating the Quality of a Knowledge Base Populated from Text—0
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension—0
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Evaluation Dataset and System for Japanese Lexical Simplification—0
Evaluation for Partial Event Coreference—0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability—0
Evaluation of Automatically Generated Pronoun Reference Questions—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
5Knowledge modelTest79.2—Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2—Unverified
7LReasoner ensembleTest76.1—Unverified
8ELECTRA and ALBERTTest71—Unverified
9WWZTest69.7—Unverified
10xlnet-large-uncased [extended data]Test69.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4—Unverified
2Megatron-BERT (ensemble)Accuracy90.9—Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8—Unverified
4Megatron-BERTAccuracy89.5—Unverified
5XLNetAccuracy (Middle)88.6—Unverified
6DeBERTalargeAccuracy86.8—Unverified
7B10-10-10Accuracy85.7—Unverified
8RoBERTaAccuracy83.2—Unverified
9Orca 2-13BAccuracy82.87—Unverified
10Orca 2-7BAccuracy80.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94—Unverified
2MT5 LargeAverage F10.84—Unverified
3ruRoberta-large finetuneAverage F10.83—Unverified
4ruT5-large-finetuneAverage F10.82—Unverified
5Human BenchmarkAverage F10.81—Unverified
6ruT5-base-finetuneAverage F10.77—Unverified
7ruBert-large finetuneAverage F10.76—Unverified
8ruBert-base finetuneAverage F10.74—Unverified
9RuGPT3XL few-shotAverage F10.74—Unverified
10RuGPT3LargeAverage F10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4—Unverified
2BERT-LargeOverall: F162.7—Unverified
3BiDAFOverall: F128.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55—Unverified