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 10511100 of 1760 papers

TitleStatusHype
Dual Multi-head Co-attention for Multi-choice Reading Comprehension0
The Shmoop Corpus: A Dataset of Stories with Loosely Aligned SummariesCode0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension0
SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis0
An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension0
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
WaLDORf: Wasteless Language-model Distillation On Reading-comprehension0
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering0
Samajh-Boojh: A Reading Comprehension system in Hindi0
Semi-supervised Visual Feature Integration for Pre-trained Language Models0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Label Dependent Deep Variational Paraphrase Generation0
JEC-QA: A Legal-Domain Question Answering Dataset0
Unsupervised Domain Adaptation of Language Models for Reading Comprehension0
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets0
Temporal Reasoning via Audio Question AnsweringCode0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension0
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering0
Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization0
Contextual Recurrent Units for Cloze-style Reading Comprehension0
Unsupervised Domain Adaptation on Reading ComprehensionCode0
Meta Answering for Machine Reading0
Knowledge Guided Text Retrieval and Reading for Open Domain Question AnsweringCode1
Improving Machine Reading Comprehension via Adversarial Training0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension0
Ask to Learn: A Study on Curiosity-driven Question Generation0
Dice Loss for Data-imbalanced NLP TasksCode0
Coreference Resolution as Query-based Span PredictionCode1
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering0
Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue0
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis0
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature0
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple DocumentsCode0
Learning with Limited Data for Multilingual Reading Comprehension0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion0
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network0
Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading ComprehensionCode0
Relation Module for Non-Answerable Predictions on Reading Comprehension0
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension0
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations0
Proceedings of the 2nd Workshop on Machine Reading for Question Answering0
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading ComprehensionCode0
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis0
Answering Naturally: Factoid to Full length Answer GenerationCode0
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension0
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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