• Corpus ID: 244896105

MetaQA: Combining Expert Agents for Multi-Skill Question Answering

@inproceedings{Puerto2021MetaQACE,
  title={MetaQA: Combining Expert Agents for Multi-Skill Question Answering},
  author={Haritz Puerto and G{\"o}zde G{\"u}l Sahin and Iryna Gurevych},
  booktitle={Conference of the European Chapter of the Association for Computational Linguistics},
  year={2021}
}

• Haritz Puerto, Gözde Gül Sahin, Iryna Gurevych
• Published in
  Conference of the European…
  3 December 2021
• Computer Science

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.