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 1151–1200 of 1760 papers

TitleStatusHype
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
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior—0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Decoding In-Context Learning: Neuroscience-inspired Analysis of Representations in Large Language Models—0
Incorporating Compositionality and Morphology into End-to-End Models—0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension—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
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text—0
Inferring Psycholinguistic Properties of Words—0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Information retrieval for label noise document ranking by bag sampling and group-wise loss—0
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—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
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension—0
InternalInspector I^2: Robust Confidence Estimation in LLMs through Internal States—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation—0
Interpretation of Natural Language Rules in Conversational Machine Reading—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Investigating Active Learning for Short-Answer Scoring—0
Investigating neural architectures for short answer scoring—0
Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension—0
Investigating the importance of linguistic complexity features across different datasets related to language learning—0
Invited Talk: Embedding Probabilistic Logic for Machine Reading—0
iPad Reading: An Innovative Approach to New Literacies—0
ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
Is It Dish Washer Safe? Automatically Answering ``Yes/No'' Questions Using Customer Reviews—0
Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models—0
It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension—0
JEC-QA: A Legal-Domain Question Answering Dataset—0
JEFF - Just Another EFFicient Reading Comprehension Test Generation—0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task—0
基於BERT模型之多國語言機器閱讀理解研究(Multilingual Machine Reading Comprehension based on BERT Model)—0
基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)—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