Controllable Semantic Parsing via Retrieval Augmentation

Panupong Pasupat, Yuan Zhang, Kelvin Guu


Abstract
In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving state-of-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.
Anthology ID:
2021.emnlp-main.607
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7683–7698
Language:
URL:
https://aclanthology.org/2021.emnlp-main.607
DOI:
10.18653/v1/2021.emnlp-main.607
Bibkey:
Cite (ACL):
Panupong Pasupat, Yuan Zhang, and Kelvin Guu. 2021. Controllable Semantic Parsing via Retrieval Augmentation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7683–7698, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Controllable Semantic Parsing via Retrieval Augmentation (Pasupat et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.607.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.607.mp4
Code
 google-research/language
Data
MTOP