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 801850 of 1760 papers

TitleStatusHype
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
Attention-Based Convolutional Neural Network for Machine Comprehension0
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
Continuous fluency tracking and the challenges of varying text complexity0
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning0
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator0
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering0
Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis0
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
Emergent Predication Structure in Hidden State Vectors of Neural Readers0
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
Emergent: a novel data-set for stance classification0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension0
Brainstorming Brings Power to Large Language Models of Knowledge Reasoning0
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model0
Embracing data abundance: BookTest Dataset for Reading Comprehension0
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
Boosting Search Engines with Interactive Agents0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge0
InternalInspector I^2: Robust Confidence Estimation in LLMs through Internal States0
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task0
BloombergGPT: A Large Language Model for Finance0
Show:102550
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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