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 551600 of 1760 papers

TitleStatusHype
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark0
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval0
A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension0
Fewer Truncations Improve Language Modeling0
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension0
CFO: A Framework for Building Production NLP Systems0
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension0
Amrita\_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension0
Few-shot Mining of Naturally Occurring Inputs and Outputs0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data0
From Light to Rich ERE: Annotation of Entities, Relations, and Events0
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs0
ARES: A Reading Comprehension Ensembling Service0
FabricQA-Extractor: A Question Answering System to Extract Information from Documents using Natural Language Questions0
AmQA: Amharic Question Answering Dataset0
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training0
Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap0
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations0
Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset0
Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges0
Eye Tracking as a Tool for Machine Translation Error Analysis0
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning0
Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education0
A machine-compiled macroevolutionary history of Phanerozoic life0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension0
A Question Answering Approach to Emotion Cause Extraction0
Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty0
CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting0
CALOR-QUEST : un corpus d'entra\^ et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)0
A Question Answering Approach for Emotion Cause Extraction0
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling0
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
Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey0
Extracting Structured Scholarly Information from the Machine Translation Literature0
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks0
Calibration of Machine Reading Systems at Scale0
Calibration of Machine Reading Systems at Scale0
A Practical Toolkit for Multilingual Question and Answer Generation0
CAESAR: Context Awareness Enabled Summary-Attentive Reader0
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension0
A linguistically-motivated evaluation methodology for unraveling model's abilities in reading comprehension tasks0
Approximating Givenness in Content Assessment through Distributional Semantics0
Exploring Probabilistic Soft Logic as a framework for integrating top-down and bottom-up processing of language in a task context0
Entity Linking meets Word Sense Disambiguation: a Unified Approach0
Entity Linking for Tweets0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension0
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering0
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis0
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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