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
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model0
GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children0
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval0
Getting the Most out of AMR Parsing0
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension0
Global memory transformer for processing long documents0
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker0
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM0
Text Understanding in GPT-4 vs Humans0
Graph-Based Knowledge Integration for Question Answering over Dialogue0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model0
Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy0
Graphical Schemes May Improve Readability but Not Understandability for People with Dyslexia0
Graph Sequential Network for Reasoning over Sequences0
Grounding Gradable Adjectives through Crowdsourcing0
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering0
Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human Literacy and Education Benchmarks0
Have You Seen That Number? Investigating Extrapolation in Question Answering Models0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension0
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension0
HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French0
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation0
Hierarchical Learning for Generation with Long Source Sequences0
Hierarchical Question Answering for Long Documents0
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models0
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs0
How Context Affects Language Models' Factual Predictions0
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks0
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?0
How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method0
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data0
IBERT: Idiom Cloze-style reading comprehension with Attention0
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications0
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks0
Identifying Where to Focus in Reading Comprehension for Neural Question Generation0
I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection0
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation0
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions0
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension0
Explicit Contextual Semantics for Text Comprehension0
Improved Synthetic Training for Reading Comprehension0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding0
Improving Cross-Lingual Reading Comprehension with Self-Training0
Improving Domain Adaptation through Extended-Text Reading Comprehension0
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation0
Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking0
Improving Machine Reading Comprehension via Adversarial Training0
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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