论文标题
披萨：复杂端到端以任务解析的新基准
PIZZA: A new benchmark for complex end-to-end task-oriented parsing
论文作者
论文摘要
最新的以任务为导向的解析工作着重于在平坦的插槽和意图之间找到中间立场，这些插槽和意图很容易注释，而有力的表示，例如lambda cyculus，lambda cyculus表现得很富有表现力，但具有昂贵的注释。本文通过引入一个用于解析披萨和饮料订单的新数据集来继续探索以任务为导向的解析，其语义不能被平坦的插槽和意图捕获。我们对该数据集的以任务为导向的解析进行了深入的评估，包括SEQ2SEQ系统和RNNGS的不同口味。该数据集有两个主要版本，一个是我们称为TOP的最近引入的话语级层次结构符号，另一个是其目标是可执行表示的（EXR）。我们从经验上证明，训练解析器直接生成EXR符号不仅解决了一个跌落中的实体分辨率问题，并克服了最高符号的许多表达局限性，而且还会导致更高的解析精度。
Much recent work in task-oriented parsing has focused on finding a middle ground between flat slots and intents, which are inexpressive but easy to annotate, and powerful representations such as the lambda calculus, which are expressive but costly to annotate. This paper continues the exploration of task-oriented parsing by introducing a new dataset for parsing pizza and drink orders, whose semantics cannot be captured by flat slots and intents. We perform an extensive evaluation of deep-learning techniques for task-oriented parsing on this dataset, including different flavors of seq2seq systems and RNNGs. The dataset comes in two main versions, one in a recently introduced utterance-level hierarchical notation that we call TOP, and one whose targets are executable representations (EXR). We demonstrate empirically that training the parser to directly generate EXR notation not only solves the problem of entity resolution in one fell swoop and overcomes a number of expressive limitations of TOP notation, but also results in significantly greater parsing accuracy.
