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 16511700 of 1760 papers

TitleStatusHype
Computing Semantic Text Similarity Using Rich Features0
Six Good Predictors of Autistic Text Comprehension0
Lexical Level Distribution of Metadiscourse in Spoken Language0
Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment0
A Strong Lexical Matching Method for the Machine Comprehension Test0
Semantic Framework for Comparison Structures in Natural Language0
Listen, Attend and SpellCode1
The ``News Web Easy'' news service as a resource for teaching and learning Japanese: An assessment of the comprehension difficulty of Japanese sentence-end expressions0
Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations0
Annotating Entailment Relations for Shortanswer Questions0
Bilingual Keyword Extraction and its Educational Application0
Machine Comprehension with Syntax, Frames, and Semantics0
Evaluation Dataset and System for Japanese Lexical Simplification0
Open IE as an Intermediate Structure for Semantic Tasks0
A Knowledge-Intensive Model for Prepositional Phrase Attachment0
Learning Answer-Entailing Structures for Machine Comprehension0
Machine Comprehension with Discourse Relations0
Teaching Machines to Read and ComprehendCode1
Judging the Quality of Automatically Generated Gap-fill Question using Active Learning0
From Light to Rich ERE: Annotation of Entities, Relations, and Events0
Neural context embeddings for automatic discovery of word senses0
CoMiC: Adapting a Short Answer Assessment System for Answer Selection0
TeamUFAL: WSD+EL as Document Retrieval0
Using Word Semantics To Assist English as a Second Language Learners0
Open Ended Intelligence: The individuation of Intelligent Agents0
Invited Talk: Embedding Probabilistic Logic for Machine Reading0
A multivariate model for classifying texts' readability0
Effective Feature Integration for Automated Short Answer Scoring0
Towards AI-Complete Question Answering: A Set of Prerequisite Toy TasksCode1
Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis0
Robust Semantics for Semantic Parsing0
Machine-guided Solution to Mathematical Word Problems0
A Keyword-based Monolingual Sentence Aligner in Text Simplification0
Paraphrase Detection for Short Answer Scoring0
Modeling Biological Processes for Reading Comprehension0
Argument structure of adverbial derivatives in Russian0
Relative clause extraction for syntactic simplification0
Assessing Conformance of Manually Simplified Corpora with User Requirements: the Case of Autistic Readers0
Focus Annotation in Reading Comprehension Data0
BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity0
Non-Monotonic Reasoning and Story Comprehension0
A machine-compiled macroevolutionary history of Phanerozoic life0
Evaluation for Partial Event Coreference0
Combining Formal and Distributional Models of Temporal and Intensional Semantics0
Low-Dimensional Embeddings of Logic0
Learning Grounded Meaning Representations with Autoencoders0
Linguistic Considerations in Automatic Question Generation0
DysList: An Annotated Resource of Dyslexic Errors0
Annotating the MASC Corpus with BabelNet0
Open-domain Interaction and Online Content in the Sami Language0
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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