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 901–950 of 1760 papers

TitleStatusHype
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks—0
Somm: Into the Model—0
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension—0
Specifying and Annotating Reduced Argument Span Via QA-SRL—0
sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting—0
Splitting Complex English Sentences—0
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0—0
Squibs: What Is a Paraphrase?—0
SRDF: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning—0
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings—0
Stars at Qur’an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur’an with Transformer Models and Releasing a New Dataset—0
Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension—0
Story Comprehension for Predicting What Happens Next—0
Structsum Generation for Faster Text Comprehension—0
Structural Characterization for Dialogue Disentanglement—0
Structural Embedding of Syntactic Trees for Machine Comprehension—0
Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Struct-X: Enhancing Large Language Models Reasoning with Structured Data—0
Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Measures—0
Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph—0
Syntactic and Lexical Approaches to Reading Comprehension—0
Syntactic Cross and Reading Effort in English to Japanese Translation—0
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
Tackling Adversarial Examples in QA via Answer Sentence Selection—0
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension—0
TangoBERT: Reducing Inference Cost by using Cascaded Architecture—0
Teach model to answer questions after comprehending the document—0
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning—0
TeamUFAL: WSD+EL as Document Retrieval—0
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension—0
TextCaps: a Dataset for Image Captioning with Reading Comprehension—0
Text Modification for Bulgarian Sign Language Users—0
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design—0
Textual complexity as a predictor of difficulty of listening items in language proficiency tests—0
Textual Entailment based Question Generation—0
The Consensus Game: Language Model Generation via Equilibrium Search—0
The C-Score -- Proposing a Reading Comprehension Metrics as a Common Evaluation Measure for Text Simplification—0
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0—0
The Global Banking Standards QA Dataset (GBS-QA)—0
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models—0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
The ``News Web Easy'' news service as a resource for teaching and learning Japanese: An assessment of the comprehension difficulty of Japanese sentence-end expressions—0
The Open Framework for Developing Knowledge Base And Question Answering System—0
The Training of Neuromodels for Machine Comprehension of Text. Brain2Text Algorithm—0
The Use of Artificial Intelligence Tools in Assessing Content Validity: A Comparative Study with Human Experts—0
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey—0
THG: Transformer with Hyperbolic Geometry—0
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—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