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 801850 of 1760 papers

TitleStatusHype
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph0
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)0
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)0
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction0
基于阅读理解的汉越跨语言新闻事件要素抽取方法(News Events Element Extraction of Chinese-Vietnamese Cross-language Using Reading Comprehension)0
BiQuAD: Towards QA based on deeper text understanding0
ECNU\_ICA\_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications0
UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning0
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension0
Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision0
Automatic Task Requirements Writing Evaluation via Machine Reading ComprehensionCode0
Human Attention during Goal-directed Reading Comprehension Relies on Task OptimizationCode0
Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking0
An Initial Investigation of Non-Native Spoken Question-Answering0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension0
What Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization0
A Search Engine for Scientific Publications: a Cybersecurity Case Study0
Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers0
Unsupervised Technique To Conversational Machine Reading0
Analyzing Research Trends in Inorganic Materials Literature Using NLPCode0
Answering Chinese Elementary School Social Study Multiple Choice Questions0
OKGIT: Open Knowledge Graph Link Prediction with Implicit TypesCode0
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales0
Open Temporal Relation Extraction for Question Answering0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task0
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?Code0
RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering0
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization0
THG: Transformer with Hyperbolic Geometry0
Towards Multi-Modal Text-Image Retrieval to improve Human Reading0
Does Structure Matter? Encoding Documents for Machine Reading Comprehension0
A Multilingual Modeling Method for Span-Extraction Reading Comprehension0
NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension0
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models0
Sentence Extraction-Based Machine Reading Comprehension for Vietnamese0
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction0
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-trainingCode0
Improving Cross-Lingual Reading Comprehension with Self-Training0
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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