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- W4206865535 abstract "The recent explosion of question answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or by combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer candidates. Through quantitative and qualitative experiments we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches in both in-domain and out-of-domain scenarios, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future developments of multi-agent systems on https://github.com/UKPLab/MetaQA." @default.
- W4206865535 created "2022-01-25" @default.
- W4206865535 creator A5027450194 @default.
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- W4206865535 date "2021-12-03" @default.
- W4206865535 modified "2023-10-18" @default.
- W4206865535 title "MetaQA: Combining Expert Agents for Multi-Skill Question Answering" @default.
- W4206865535 doi "https://doi.org/10.48550/arxiv.2112.01922" @default.
- W4206865535 hasPublicationYear "2021" @default.
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