Deep learning methods have enabled task-oriented semantic parsing of
increasingly complex utterances. However, a single model is still typically
trained and deployed for each task separately, requiring labeled training data
for each, which makes it challenging to support new tasks, even within a single
business vertical (e.g., food-ordering or travel booking). In this paper we
describe Cross-TOP (Cross-Schema Task-Oriented Parsing), a zero-shot method for
complex semantic parsing in a given vertical. By leveraging the fact that user
requests from the same vertical share lexical and semantic similarities, a
single cross-schema parser is trained to service an arbitrary number of tasks,
seen or unseen, within a vertical. We show that Cross-TOP can achieve high
accuracy on a previously unseen task without requiring any additional training
data, thereby providing a scalable way to bootstrap semantic parsers for new
tasks. As part of this work we release the FoodOrdering dataset, a
task-oriented parsing dataset in the food-ordering vertical, with utterances
and annotations derived from five schemas, each from a different restaurant
menu.

本文介绍了一种名为 Cross-TOP 的零 - shot 方法，通过利用用户请求的词汇和语义相似性，训练单个交叉架构解析器以服务于垂直内的任意数量的任务，从而为新任务引导语义解析器提供可扩展的方式。