Reading Comprehension
568 papers with code • 7 benchmarks • 95 datasets
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
Benchmarks
These leaderboards are used to track progress in Reading Comprehension
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Libraries
Use these libraries to find Reading Comprehension models and implementations
SQuAD
MS MARCO
TriviaQA
HotpotQA
RACE
NewsQA
CoQA
XQuAD
QuAC
TyDiQA
Subtasks
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Machine Reading Comprehension
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Intent Recognition
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Implicit Relations
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LAMBADA
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LAMBADA
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Question Selection
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Multi-Hop Reading Comprehension
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Implicatures
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Logical Reasoning Reading Comprehension
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English Proverbs
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Fantasy Reasoning
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Figure Of Speech Detection
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Formal Fallacies Syllogisms Negation
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GRE Reading Comprehension
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Hyperbaton
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Movie Dialog Same Or Different
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Nonsense Words Grammar
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Phrase Relatedness
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RACE-h
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RACE-m
Latest papers
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images
Visual Question Answering (VQA) is a complicated task that requires the capability of simultaneously processing natural language and images.
NoticIA: A Clickbait Article Summarization Dataset in Spanish
We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans.
Interpreting Themes from Educational Stories
Reading comprehension continues to be a crucial research focus in the NLP community.
KazQAD: Kazakh Open-Domain Question Answering Dataset
We introduce KazQAD -- a Kazakh open-domain question answering (ODQA) dataset -- that can be used in both reading comprehension and full ODQA settings, as well as for information retrieval experiments.
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
ST-LLM: Large Language Models Are Effective Temporal Learners
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
Latxa: An Open Language Model and Evaluation Suite for Basque
We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters.
ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP.
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages
Therefore, to enable realistic testing of QA models, our dataset can be used in three different ways: answering questions from raw and noisy content, answering questions from cleaner, corrected version of the content, as well as answering questions from scanned images of newspaper pages.
WangchanLion and WangchanX MRC Eval
Our model is based on SEA-LION and a collection of instruction following datasets.
