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 10011050 of 1760 papers

TitleStatusHype
University Entrance Examinations as a Benchmark Resource for NLP-based Problem Solving0
Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models0
Unlocking the Potential of Multiple BERT Models for Bangla Question Answering in NCTB Textbooks0
Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks0
Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding0
Unsupervised Domain Adaptation of Language Models for Reading Comprehension0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data0
Unsupervised Explanation Generation for Machine Reading Comprehension0
Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates0
Unsupervised Open-Domain Question Answering0
Unsupervised Open-Domain Question Answering with Higher Answerability0
Unsupervised Technique To Conversational Machine Reading0
Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in Procedural Text Comprehension0
UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning0
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models0
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L20
Using calibrator to improve robustness in Machine Reading Comprehension0
Using Finite State Transducers for Making Efficient Reading Comprehension Dictionaries0
Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions0
Using the text to evaluate short answers for reading comprehension exercises0
Using Word Semantics To Assist English as a Second Language Learners0
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension0
VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond0
ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese0
Virtual Pre-Service Teacher Assessment and Feedback via Conversational Agents0
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension0
Visualizing attention zones in machine reading comprehension models0
Visual Question Answering as Reading Comprehension0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension0
WaLDORf: Wasteless Language-model Distillation On Reading-comprehension0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning0
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading0
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
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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