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

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Showing 801–850 of 1760 papers

TitleStatusHype
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?—0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding—0
Improving Cross-Lingual Reading Comprehension with Self-Training—0
Improving Domain Adaptation through Extended-Text Reading Comprehension—0
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation—0
Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking—0
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering—0
Computing Semantic Text Similarity Using Rich Features—0
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite—0
Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
A Tagging Approach to Identify Complex Constituents for Text Simplification—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
Advances in Multi-turn Dialogue Comprehension: A Survey—0
Computational Approaches to Sentence Completion—0
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks—0
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset—0
How Context Affects Language Models' Factual Predictions—0
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs—0
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation—0
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models—0
Incorporating External Knowledge into Machine Reading for Generative Question Answering—0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
InDEX: Indonesian Idiom and Expression Dataset for Cloze Test—0
Hierarchical Question Answering for Long Documents—0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension—0
A Survey on Neural Machine Reading Comprehension—0
Information retrieval for label noise document ranking by bag sampling and group-wise loss—0
Hierarchical Learning for Generation with Long Source Sequences—0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—0
Hierarchical Evaluation Framework: Best Practices for Human Evaluation—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining—0
Integrated Triaging for Fast Reading Comprehension—0
Semi-supervised Visual Feature Integration for Pre-trained Language Models—0
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model—0
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content—0
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension—0
HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French—0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text—0
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension—0
ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension—0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text—0
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
5Knowledge modelTest79.2—Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2—Unverified
7LReasoner ensembleTest76.1—Unverified
8ELECTRA and ALBERTTest71—Unverified
9WWZTest69.7—Unverified
10xlnet-large-uncased [extended data]Test69.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4—Unverified
2Megatron-BERT (ensemble)Accuracy90.9—Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8—Unverified
4Megatron-BERTAccuracy89.5—Unverified
5XLNetAccuracy (Middle)88.6—Unverified
6DeBERTalargeAccuracy86.8—Unverified
7B10-10-10Accuracy85.7—Unverified
8RoBERTaAccuracy83.2—Unverified
9Orca 2-13BAccuracy82.87—Unverified
10Orca 2-7BAccuracy80.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94—Unverified
2MT5 LargeAverage F10.84—Unverified
3ruRoberta-large finetuneAverage F10.83—Unverified
4ruT5-large-finetuneAverage F10.82—Unverified
5Human BenchmarkAverage F10.81—Unverified
6ruT5-base-finetuneAverage F10.77—Unverified
7ruBert-large finetuneAverage F10.76—Unverified
8ruBert-base finetuneAverage F10.74—Unverified
9RuGPT3XL few-shotAverage F10.74—Unverified
10RuGPT3LargeAverage F10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4—Unverified
2BERT-LargeOverall: F162.7—Unverified
3BiDAFOverall: F128.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55—Unverified