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 601–650 of 1760 papers

TitleStatusHype
Calibration of Machine Reading Systems at Scale—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
Calibration of Machine Reading Systems at Scale—0
A Practical Toolkit for Multilingual Question and Answer Generation—0
CAESAR: Context Awareness Enabled Summary-Attentive Reader—0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
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
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations—0
Evaluating a How-to Tip Machine Comprehension Model with QA Examples collected from a Community QA Site—0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples—0
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
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers—0
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting—0
Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation—0
A Question Answering Approach to Emotion Cause Extraction—0
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
Evaluating Machine Reading Systems through Comprehension Tests—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
A linguistically-motivated evaluation methodology for unraveling model's abilities in reading comprehension tasks—0
Evaluating Neural Model Robustness for Machine Comprehension—0
Approximating Givenness in Content Assessment through Distributional Semantics—0
From Light to Rich ERE: Annotation of Entities, Relations, and Events—0
Entity Linking meets Word Sense Disambiguation: a Unified Approach—0
Evaluating the Meaning of Answers to Reading Comprehension Questions: A Semantics-Based Approach—0
Entity Linking for Tweets—0
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension—0
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
Evaluation Dataset and System for Japanese Lexical Simplification—0
Evaluation for Partial Event Coreference—0
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension—0
Evaluation of Automatically Generated Pronoun Reference Questions—0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering—0
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis—0
CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs—0
Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systems—0
Building A User-Centric and Content-Driven Socialbot—0
Event Extraction as Multi-turn Question Answering—0
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text—0
CFO: A Framework for Building Production NLP Systems—0
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker—0
Ensemble approach for natural language question answering problem—0
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19—0
LLM-aided explanations of EDA synthesis errors—0
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval—0
Explanation Generation for a Math Word Problem Solver—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—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