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 16511700 of 1760 papers

TitleStatusHype
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
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite0
Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior0
Improving Zero-Shot Event Extraction via Sentence Simplification0
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models0
Incorporating Compositionality and Morphology into End-to-End Models0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension0
Incorporating External Knowledge into Machine Reading for Generative Question Answering0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference0
InDEX: Indonesian Idiom and Expression Dataset for Cloze Test0
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text0
Inferring Psycholinguistic Properties of Words0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups0
Information retrieval for label noise document ranking by bag sampling and group-wise loss0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension0
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining0
Integrated Triaging for Fast Reading Comprehension0
Semi-supervised Visual Feature Integration for Pre-trained Language Models0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model0
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension0
InternalInspector I^2: Robust Confidence Estimation in LLMs through Internal States0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation0
Interpretation of Natural Language Rules in Conversational Machine Reading0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension0
Investigating Active Learning for Short-Answer Scoring0
Investigating neural architectures for short answer scoring0
Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension0
Investigating the importance of linguistic complexity features across different datasets related to language learning0
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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