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 401–450 of 1760 papers

TitleStatusHype
Context-Paraphrase Enhanced Commonsense Question Answering—0
Contextualized Representations Using Textual Encyclopedic Knowledge—0
Attention-Based Convolutional Neural Network for Machine Comprehension—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
Continual Domain Adaptation for Machine Reading Comprehension—0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation—0
Continuous fluency tracking and the challenges of varying text complexity—0
Do Chinese models speak Chinese languages?—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models—0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text—0
Controlling Risk of Web Question Answering—0
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering—0
Conversational Machine Comprehension: a Literature Review—0
Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty—0
An Effective Multi-Stage Approach For Question Answering—0
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Combining Formal and Distributional Models of Temporal and Intensional Semantics—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics—0
A Unified Abstractive Model for Generating Question-Answer Pairs—0
Assessing Distractors in Multiple-Choice Tests—0
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop—0
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training—0
Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers—0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning—0
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search—0
Creating Interactive Macaronic Interfaces for Language Learning—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Coherent Zero-Shot Visual Instruction Generation—0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora—0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Automated Pyramid Scoring of Summaries using Distributional Semantics—0
Assessing Chinese Readability using Term Frequency and Lexical Chain—0
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
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 Comprehension—0
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups—0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
Data Augmentation for Biomedical Factoid Question Answering—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
A New Semantic Lexicon and Similarity Measure in Bangla—0
Data-Driven Metaphor Recognition and Explanation—0
Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—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