论文标题
GAAMA 2.0：回答布尔和提取性问题的集成系统
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
论文作者
论文摘要
最近的机器阅读理解数据集包括提取和布尔值问题，但当前的方法并未为回答这两种问题类型提供综合支持。我们提出了一个多语言的机器阅读理解系统和前端演示，该演示通过提供“是/否答案”并突出支持证据，并通过突出段落中的答案来处理提取问题，从而解决布尔值。在撰写本文时，我们的系统GAAMA 2.0在TYDI QA排行榜上排名第一。我们对比了我们方法的两种不同的实现。第一个包括几个独立的变压器堆栈，可以轻松部署每个组件。第二个是使用适配器在资源约束环境中减少GPU内存足迹的单一堆栈。
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a multilingual machine reading comprehension system and front-end demo that handles boolean questions by providing both a YES/NO answer and highlighting supporting evidence, and handles extractive questions by highlighting the answer in the passage. Our system, GAAMA 2.0, is ranked first on the Tydi QA leaderboard at the time of this writing. We contrast two different implementations of our approach. The first includes several independent stacks of transformers allowing easy deployment of each component. The second is a single stack of transformers utilizing adapters to reduce GPU memory footprint in a resource-constrained environment.
