Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing

Melanie Rubino, Nicolas Guenon des Mesnards, Uday Shah, Nanjiang Jiang, Weiqi Sun, Konstantine Arkoudas


Abstract
Deep learning methods have enabled taskoriented 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.
Anthology ID:
2022.deeplo-1.6
Volume:
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing
Month:
July
Year:
2022
Address:
Hybrid
Editors:
Colin Cherry, Angela Fan, George Foster, Gholamreza (Reza) Haffari, Shahram Khadivi, Nanyun (Violet) Peng, Xiang Ren, Ehsan Shareghi, Swabha Swayamdipta
Venue:
DeepLo
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
48–60
Language:
URL:
https://aclanthology.org/2022.deeplo-1.6
DOI:
10.18653/v1/2022.deeplo-1.6
Bibkey:
Cite (ACL):
Melanie Rubino, Nicolas Guenon des Mesnards, Uday Shah, Nanjiang Jiang, Weiqi Sun, and Konstantine Arkoudas. 2022. Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing. In Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing, pages 48–60, Hybrid. Association for Computational Linguistics.
Cite (Informal):
Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing (Rubino et al., DeepLo 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.deeplo-1.6.pdf
Video:
 https://aclanthology.org/2022.deeplo-1.6.mp4