TWEAC：具有可扩展QA代理分类器的Transformer
TWEAC: Transformer with Extendable QA Agent Classifiers
摘要：
问答系统应该帮助用户获取关于广泛主题的知识，并回答各种不同的问题。大多数系统都达不到这一期望，因为它们只专门用于一种特定的设置，例如，用维基百科的数据回答事实问题。为了克服这一限制，我们建议组成一个元QA系统内的多个QA代理。我们认为，存在着广泛的专业QA代理文献。因此，我们解决的中心研究问题，如何有效地和高效地确定合适的QA代理任何给定的问题。我们研究了有监督和无监督的方法来解决这一挑战，表明TWEAC --具有可扩展代理分类器的Transformer--以94%的准确率实现了最佳的整体性能。我们对TWEAC的可扩展性提供了广泛的见解，证明它可以强大地扩展到100多个QA代理，每个代理只提供1000个他们可以回答的问题的例子。我们的代码和数据可在https://github.com/UKPLab/TWEAC-qa-agent-selection上找到
英文摘要：
Question answering systems should help users to access knowledge on a broad range of topics and to answer a wide array of different questions. Most systems fall short of this expectation as they are only specialized in one particular setting, e.g., answering factual questions with Wikipedia data. To overcome this limitation, we propose composing multiple QA agents within a meta-QA system. We argue that there exist a wide range of specialized QA agents in literature. Thus, we address the central research question of how to effectively and efficiently identify suitable QA agents for any given question. We study both supervised and unsupervised approaches to address this challenge, showing that TWEAC -- Transformer with Extendable Agent Classifiers -- achieves the best performance overall with 94% accuracy. We provide extensive insights on the scalability of TWEAC, demonstrating that it scales robustly to over 100 QA agents with each providing just 1000 examples of questions they can answer. Our code and data is available: https://github.com/UKPLab/TWEAC-qa-agent-selection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
