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 426450 of 1760 papers

TitleStatusHype
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training0
Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning0
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search0
Creating Interactive Macaronic Interfaces for Language Learning0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets0
Coherent Zero-Shot Visual Instruction Generation0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation0
Automated Pyramid Scoring of Summaries using Distributional Semantics0
Assessing Chinese Readability using Term Frequency and Lexical Chain0
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking0
Analyse automatique en cadres s\'emantiques pour l'apprentissage de mod\`eles de compr\'ehension de texte (Semantic Frame Parsing for training Machine Reading Comprehension models)0
CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?0
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension0
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension0
Data Augmentation for Biomedical Factoid Question Answering0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension0
A New Semantic Lexicon and Similarity Measure in Bangla0
Data-Driven Metaphor Recognition and Explanation0
Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation0
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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