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        <title>MetaQA: Combining Expert Agents for Multi-Skill Question Answering</title>
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    <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 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 predictions. 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, 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 research of multi-agent systems.</abstract>
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%0 Conference Proceedings
%T MetaQA: Combining Expert Agents for Multi-Skill Question Answering
%A Puerto, Haritz
%A Şahin, Gözde
%A Gurevych, Iryna
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F puerto-etal-2023-metaqa
%X 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 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 predictions. 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, 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 research of multi-agent systems.
%R 10.18653/v1/2023.eacl-main.259
%U https://aclanthology.org/2023.eacl-main.259
%U https://doi.org/10.18653/v1/2023.eacl-main.259
%P 3566-3580

Markdown (Informal)

[MetaQA: Combining Expert Agents for Multi-Skill Question Answering](https://aclanthology.org/2023.eacl-main.259) (Puerto et al., EACL 2023)

• MetaQA: Combining Expert Agents for Multi-Skill Question Answering (Puerto et al., EACL 2023)

ACL

• Haritz Puerto, Gözde Şahin, and Iryna Gurevych. 2023. MetaQA: Combining Expert Agents for Multi-Skill Question Answering. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3566–3580, Dubrovnik, Croatia. Association for Computational Linguistics.