论文标题
FLIN：网络导航的灵活自然语言接口
FLIN: A Flexible Natural Language Interface for Web Navigation
论文作者
论文摘要
AI助手现在可以通过直接与网站UI进行交互来执行用户的任务。当前的语义解析和插槽填充技术不能在不经常训练的情况下灵活地适应许多不同的网站。我们建议使用Web导航的自然语言接口Flin，该界面将用户命令映射到概念级别的操作（而不是低级UI操作），从而能够灵活地适应不同的网站并处理其瞬态性质。我们将其视为排名问题：给定一个用户命令和网页，Flin学会了为最相关的导航指令（涉及操作和参数值）评分。为了培训和评估FLIN，我们使用来自三个域的九个流行网站收集数据集。我们的结果表明，Flin能够适应给定域中的新网站。
AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained. We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem: given a user command and a webpage, FLIN learns to score the most relevant navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three domains. Our results show that FLIN was able to adapt to new websites in a given domain.
