416 datasets found

Groups: Question Answering Organizations: No Organization

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  • Question Classification using Convolutional Neural Networks

    Question classification using Convolutional Neural Networks

  • CLEVR

    CLEVR images contain objects characterized by a set of attributes (shape, color, size and material). The questions are grouped into 5 categories: Exist, Count, CompareInteger,...

  • TREC

    The dataset used for sentiment analysis, question type classification, and subjectivity classification tasks.

  • DocVQA and ChartQA Datasets

    The dataset used for testing the Vary-base model, containing DocVQA and ChartQA datasets.

  • Training Language Models to Perform Tasks

    A dataset for training language models to perform tasks such as question answering and text classification.

  • Visual Genome

    The Visual Genome dataset is a large-scale visual question answering dataset, containing 1.5 million images, each with 15-30 annotated entities, attributes, and relationships.

  • COCO-QA

    The COCO-QA dataset is used for visual question answering task. It consists of 123,287 images and 78,736 train and 38,948 test questions.

  • SimpleQuestion dataset for Wikidata

    The dataset used in this paper is a reinforcement learning dataset, specifically the SimpleQuestion dataset, which contains questions answerable using Wikidata as the knowledge...

  • Seq2SQL

    Seq2SQL: Generating structured queries from natural language using reinforcement learning.

  • WikiTableQuestions

    Semantic parsing maps a user-issued natural language (NL) utterance to a machine-executable meaning representation (MR), such as λ−calculus (Zettlemoyer and Collins, 2005), SQL...

  • ToolWriter: Generating query-specific tools for tabular question answering

    Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data.

  • AlpacaFarm

    The AlpacaFarm dataset is a large-scale dataset for preference optimization, which consists of a set of instructions and their corresponding responses.

  • QuaRTz

    The dataset used in the paper to evaluate the REFLEX system, consisting of open-domain qualitative relationship questions.

  • OBQA

    The dataset used in the paper to evaluate the REFLEX system, consisting of open-book question answering tasks.

  • EntailmentBank

    The dataset used in the paper to evaluate the REFLEX system, consisting of multiple-choice questions with entailment relationships.

  • SentEval

    The SentEval dataset is a library for evaluating the quality of sentence embeddings.
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