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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1250 of 1760 papers

TitleStatusHype
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)—0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)—0
基于阅读理解的汉越跨语言新闻事件要素抽取方法(News Events Element Extraction of Chinese-Vietnamese Cross-language Using Reading Comprehension)—0
基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)—0
Joint Inference for Event Timeline Construction—0
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension—0
Judging the Quality of Automatically Generated Gap-fill Question using Active Learning—0
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language—0
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation—0
Keyword Highlighting Improves Comprehension for People with Dyslexia—0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning—0
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information—0
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator—0
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge—0
Knowledge-Aided Open-Domain Question Answering—0
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge—0
Knowledge Based Machine Reading Comprehension—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering—0
Knowledge Efficient Deep Learning for Natural Language Processing—0
Know your tools well: Better and faster QA with synthetic examples—0
Korean FrameNet Expansion Based on Projection of Japanese FrameNet—0
Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?—0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension—0
KOSMOS-2.5: A Multimodal Literate Model—0
Label Dependent Deep Variational Paraphrase Generation—0
LagunTest: A NLP Based Application to Enhance Reading Comprehension—0
Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering—0
Large Language Models are Null-Shot Learners—0
Large Language Models as Misleading Assistants in Conversation—0
Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis—0
Latent Question Reformulation and Information Accumulation for Multi-Hop Machine Reading—0
LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation—0
LC-Score: Reference-less estimation of Text Comprehension Difficulty—0
Learning and Knowledge Transfer with Memory Networks for Machine Comprehension—0
Learning Answer-Entailing Structures for Machine Comprehension—0
Learning-based Multi-Sieve Co-reference Resolution with Knowledge—0
Learning from Demonstration with Weakly Supervised Disentanglement—0
Learning Grounded Meaning Representations with Autoencoders—0
Learning Open Domain Multi-hop Search Using Reinforcement Learning—0
Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets—0
Learning Representations for Zero-Shot Retrieval over Structured Data—0
Learning to Ask Unanswerable Questions for Machine Reading Comprehension—0
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training—0
Learning to Compute Word Embeddings On the Fly—0
Learning to Generate Questions by Recovering Answer-containing Sentences—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