Controllable Semantic Parsing via Retrieval Augmentation

Ice Pasupat
Yuan Zhang


In practical applications of semantic parsing, we occasionally want to control the behavior of the parser, such as making it output meaning representations in a new domain, or influencing the prediction on some queries toward certain patterns. While it is possible to fine-tune the parser on examples exhibiting the target behavior, a method that does not consume as much time or computation resources would be preferable. To this end, we propose retrieval-augmented generative semantic parser (RAG-SP): given the input query, the parser retrieves relevant information from the retrieval index, augment it to the query, and then apply a generative model to produce an output. The augmented information acts as a soft influence on the generative model, and 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 RAG-SP can parse queries in a new domain or adapt the prediction toward the specified patterns without having to fine-tune the model. With some modifications, RAG-SP also performs well on the episodic few-shot setup on the SNIPS slot tagging dataset.

Research Areas