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 1251–1300 of 1760 papers

TitleStatusHype
Learning to Jointly Predict Ellipsis and Comparison Structures—0
Learning to ``Read Between the Lines'' using Bayesian Logic Programs—0
Learning to Reason With Adaptive Computation—0
Learning what to read: Focused machine reading—0
Learning with Instance Bundles for Reading Comprehension—0
Learning with Limited Data for Multilingual Reading Comprehension—0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension—0
Lecture et prosodie chez l'enfant dyslexique, le cas des pauses (Reading and prosody in dyslexic children, pause patterns) [in French]—0
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
Leveraging Human Feedback to Scale Educational Datasets: Combining Crowdworkers and Comparative Judgement—0
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading—0
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation—0
Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval—0
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification—0
Lexical Complexity Prediction: An Overview—0
Lexical Level Distribution of Metadiscourse in Spoken Language—0
Lightweight Convolutional Approaches to Reading Comprehension on SQuAD—0
Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions—0
Linguistic Considerations in Automatic Question Generation—0
Linguistic Knowledge as Memory for Recurrent Neural Networks—0
Listening Comprehension over Argumentative Content—0
ListReader: Extracting List-form Answers for Opinion Questions—0
LK2022 at Qur’an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur’an—0
LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements—0
Locke’s Holiday: Belief Bias in Machine Reading—0
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification—0
Logic Pre-Training of Language Models—0
Long-Distance Time-Event Relation Extraction—0
LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion—0
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation—0
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization—0
Looking Beyond Short-Premise Natural Language Inference for Downstream Tasks—0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences—0
Look Within, Why LLMs Hallucinate: A Causal Perspective—0
Low-Dimensional Embeddings of Logic—0
Low-Resource Generation of Multi-hop Reasoning Questions—0
LSDSem 2017 Shared Task: The Story Cloze Test—0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models—0
Machine Comprehension Based on Learning to Rank—0
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis—0
Machine Comprehension using Rich Semantic Representations—0
Machine Comprehension with Discourse Relations—0
Machine Comprehension with Syntax, Frames, and Semantics—0
Machine-guided Solution to Mathematical Word Problems—0
Machine Reading Comprehension: a Literature Review—0
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Machine Reading Comprehension: Generative or Extractive Reader?—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