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 13011350 of 1760 papers

TitleStatusHype
Improving Machine Reading Comprehension with General Reading StrategiesCode0
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension0
Are you tough enough? Framework for Robustness Validation of Machine Comprehension SystemsCode0
Lightweight Convolutional Approaches to Reading Comprehension on SQuAD0
A Span-Extraction Dataset for Chinese Machine Reading ComprehensionCode0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension0
U-Net: Machine Reading Comprehension with Unanswerable QuestionsCode0
Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
Entity Tracking Improves Cloze-style Reading ComprehensionCode0
未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension) [In Chinese]0
Commonsense Knowledge Base Completion and Generation0
A Nil-Aware Answer Extraction Framework for Question AnsweringCode0
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller0
QuAC: Question Answering in Context0
Speed Reading: Learning to Read ForBackward via ShuttleCode0
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension0
Answer-focused and Position-aware Neural Question Generation0
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension0
Somm: Into the Model0
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention NetworksCode1
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension0
Recovering Missing Characters in Old Hawaiian Writing0
Listening Comprehension over Argumentative Content0
A dataset and baselines for sequential open-domain question answering0
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question AnsweringCode0
Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering0
Stochastic Answer Networks for SQuAD 2.0Code0
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements0
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
Generating Distractors for Reading Comprehension Questions from Real ExaminationsCode1
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
Development and Evaluation of a Personalized Computer-aided Question Generation for English Learners to Improve Proficiency and Correct Mistakes0
Question Answering by Reasoning Across Documents with Graph Convolutional NetworksCode0
Reasoning about Actions and State Changes by Injecting Commonsense KnowledgeCode0
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
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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