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 11011150 of 1760 papers

TitleStatusHype
Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report0
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks0
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense0
CLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding0
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior0
Cross-Task Knowledge Transfer for Query-Based Text Summarization0
On Making Reading Comprehension More Comprehensive0
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?0
A Unified MRC Framework for Named Entity RecognitionCode1
Relation Module for Non-answerable Prediction on Question Answering0
Capturing Greater Context for Question GenerationCode0
MRQA 2019 Shared Task: Evaluating Generalization in Reading ComprehensionCode0
Why can't memory networks read effectively?0
NumNet: Machine Reading Comprehension with Numerical ReasoningCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
Multilingual Question Answering from Formatted Text applied to Conversational Agents0
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason0
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State TrackingCode1
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference0
基於BERT模型之多國語言機器閱讀理解研究(Multilingual Machine Reading Comprehension based on BERT Model)0
Real World Voice Assistant System for Cooking0
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks0
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
A Simple and Effective Model for Answering Multi-span QuestionsCode0
Integrated Triaging for Fast Reading Comprehension0
Multi-Modal Citizen Science: From Disambiguation to Transcription of Classical Literature0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion0
Latent Question Reformulation and Information Accumulation for Multi-Hop Machine Reading0
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering0
Question Answering is a Format; When is it Useful?0
What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question AnsweringCode0
Look, Read and Enrich. Learning from Scientific Figures and their CaptionsCode0
AllenNLP Interpret: A Framework for Explaining Predictions of NLP ModelsCode0
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model ParallelismCode2
Revealing the Importance of Semantic Retrieval for Machine Reading at ScaleCode1
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension0
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model0
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text0
Question Generation by TransformersCode0
Picture What you ReadCode0
Span Selection Pre-training for Question AnsweringCode0
Symmetric Regularization based BERT for Pair-wise Semantic ReasoningCode0
Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test0
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression0
Incorporating External Knowledge into Machine Reading for Generative Question Answering0
Reading Comprehension Ability Test-A Turing Test for Reading Comprehension0
Semantics-aware BERT for Language UnderstandingCode0
Neural Network-based Models with Commonsense Knowledge for 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