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 13511400 of 1760 papers

TitleStatusHype
Multi-task Learning with Sample Re-weighting for Machine Reading ComprehensionCode0
Automatic Judgment Prediction via Legal Reading Comprehension0
Commonsense for Generative Multi-Hop Question Answering TasksCode0
Knowledge Based Machine Reading Comprehension0
Explicit Utilization of General Knowledge in Machine Reading Comprehension0
Multilingual Extractive Reading Comprehension by Runtime Machine TranslationCode0
Explicit Contextual Semantics for Text Comprehension0
Dual Ask-Answer Network for Machine Reading ComprehensionCode0
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks0
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes0
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension0
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting0
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension0
Question Answering by Reasoning Across Documents with Graph Convolutional NetworksCode0
Reasoning about Actions and State Changes by Injecting Commonsense KnowledgeCode0
Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes0
Interpretation of Natural Language Rules in Conversational Machine Reading0
What Makes Reading Comprehension Questions Easier?Code0
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question AnsweringCode0
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading ComprehensionCode0
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision0
Attention-Guided Answer Distillation for Machine Reading Comprehension0
CoQA: A Conversational Question Answering ChallengeCode0
QuAC : Question Answering in Context0
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension0
Read + Verify: Machine Reading Comprehension with Unanswerable Questions0
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks0
Hierarchical Attention: What Really Counts in Various NLP TasksCode0
Effective Character-augmented Word Embedding for Machine Reading Comprehension0
ODSQA: Open-domain Spoken Question Answering DatasetCode0
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension0
Investigating the importance of linguistic complexity features across different datasets related to language learning0
Reading Comprehension with Graph-based Temporal-Casual Reasoning0
An Annotated Corpus of Picture Stories Retold by Language Learners0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge0
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network0
MCDTB: A Macro-level Chinese Discourse TreeBank0
Modeling Task Effects in Human Reading with Neural Network-based Attention0
Repartitioning of the ComplexWebQuestions DatasetCode0
Question-Aware Sentence Gating Networks for Question and Answering0
Difficulty Controllable Generation of Reading Comprehension Questions0
Proceedings of the Workshop on Machine Reading for Question Answering0
Systematic Error Analysis of the Stanford Question Answering Dataset0
Generating Questions for Reading Comprehension using Coherence Relations0
Syntactic and Lexical Approaches to Reading Comprehension0
CNN for Text-Based Multiple Choice Question AnsweringCode0
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base0
Document Modeling with External Attention for Sentence ExtractionCode0
Denoising Distantly Supervised Open-Domain Question AnsweringCode0
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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