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
未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension)0
Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents0
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?0
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?0
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text0
What Has Been Lost with Synthetic Evaluation?0
What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension0
What is Missing in Existing Multi-hop Datasets? Toward Deeper Multi-hop Reasoning Task0
What Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification0
What Makes it Difficult to Understand a Scientific Literature?0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types0
What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types0
What's the Meaning of Superhuman Performance in Today's NLU?0
When Did that Happen? --- Linking Events and Relations to Timestamps0
When Do Decompositions Help for Machine Reading?0
Who did What: A Large-Scale Person-Centered Cloze Dataset0
Why can't memory networks read effectively?0
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts0
Work Smart - Reducing Effort in Short-Answer Grading0
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions0
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension0
XL^2Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies0
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking0
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches0
Y-NQ: English-Yorùbá Evaluation dataset for Open-Book Reading Comprehension and Text Generation0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble0
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension0
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge0
``You are grounded!'': Latent Name Artifacts in Pre-trained Language Models0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization0
Zero-shot Event Causality Identification with Question Answering0
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models0
Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks0
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level0
SAT3D: Image-driven Semantic Attribute Transfer in 3D0
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets0
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models0
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction0
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval0
2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset0
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension0
App-Aware Response Synthesis for User Reviews0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts0
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents0
未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An OOV Word Embedding Framework for Chinese Machine Reading Comprehension) [In Chinese]0
AceMap: Knowledge Discovery through Academic Graph0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
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