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
Zero-shot Event Causality Identification with Question Answering0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data0
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
Trigger-free Event Detection via Derangement Reading Comprehension0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text0
MRCLens: an MRC Dataset Bias Detection Toolkit0
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
Automatic True/False Question Generation for Educational Purpose0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
Educational Multi-Question Generation for Reading ComprehensionCode0
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions0
Task Transfer and Domain Adaptation for Zero-Shot Question AnsweringCode0
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations0
FQuAD2.0: French Question Answering and Learning When You Don’t Know0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT0
niksss at Qur’an QA 2022: A Heavily Optimized BERT Based Model for Answering Questions from the Holy Qu’ran0
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model0
HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French0
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an0
Question Generation and Answering for exploring Digital Humanities collections0
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method0
Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension0
Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers0
MOTIF: Contextualized Images for Complex Words to Improve Human Reading0
LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an0
DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Learning Open Domain Multi-hop Search Using Reinforcement Learning0
A Fine-grained Interpretability Evaluation Benchmark for Neural NLP0
On the Trade-off between Redundancy and Local Coherence in SummarizationCode0
Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks0
Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering0
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language ModelsCode0
NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
Few-shot Mining of Naturally Occurring Inputs and Outputs0
Better Retrieval May Not Lead to Better Question Answering0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering0
KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language0
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question GenerationCode0
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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