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 15011550 of 1760 papers

TitleStatusHype
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks0
Multi-Mention Learning for Reading Comprehension with Neural Cascades0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification0
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups0
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L20
Sentence Complexity Estimation for Chinese-speaking Learners of Japanese0
Phase Conductor on Multi-layered Attentions for Machine Comprehension0
Constructing Datasets for Multi-hop Reading Comprehension Across Documents0
Smarnet: Teaching Machines to Read and Comprehend Like Human0
A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension0
A Question Answering Approach for Emotion Cause Extraction0
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions0
Discourse-Wide Extraction of Assay Frames from the Biological Literature0
Continuous fluency tracking and the challenges of varying text complexity0
Evaluation of Automatically Generated Pronoun Reference Questions0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation0
Splitting Complex English Sentences0
Dict2vec : Learning Word Embeddings using Lexical DictionariesCode0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification0
Learning what to read: Focused machine reading0
Story Comprehension for Predicting What Happens Next0
Simplifying metaphorical language for young readers: A corpus study on news text0
Investigating neural architectures for short answer scoring0
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents0
Getting the Most out of AMR Parsing0
Question Generation for Question Answering0
R^3: Reinforced Reader-Ranker for Open-Domain Question AnsweringCode0
A Question Answering Approach to Emotion Cause Extraction0
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator0
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension0
Question Dependent Recurrent Entity Network for Question AnsweringCode0
Adversarial Examples for Evaluating Reading Comprehension SystemsCode0
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability0
A Constituent-Centric Neural Architecture for Reading Comprehension0
Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task0
Coarse-to-Fine Question Answering for Long Documents0
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension0
Benben: A Chinese Intelligent Conversational Robot0
Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph0
Gated Self-Matching Networks for Reading Comprehension and Question Answering0
Two-Stage Synthesis Networks for Transfer Learning in Machine ComprehensionCode0
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension0
Neural Models for Key Phrase Detection and Question Generation0
Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models0
A Joint Model for Question Answering and Question Generation0
Learning to Compute Word Embeddings On the Fly0
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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