Controllable Semantic Parsing via Retrieval Augmentation
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.
Code (1)
Tasks
RetrievalSemantic ParsingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hierarchical Neural Data Synthesis for Semantic Parsing
Semantic parsing datasets are expensive to collect. Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily available, especially in cross-domain s…
Data AugmentationSemantic ParsingText to SQLText-To-SQLHolistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the complex parsing task into semantically appr…
AttributeData AugmentationSemantic ParsingRetrieval-Augmented Semantic Parsing: Using Large Language Models to Improve Generalization
Open-domain semantic parsing remains a challenging task, as models often rely on heuristics and struggle to handle unseen concepts. In this paper, we investigate the potential of large language models (LLMs) for this tas…
DecoderRetrievalSemantic ParsingGenerate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
A common recent approach to semantic parsing augments sequence-to-sequence models by retrieving and appending a set of training samples, called exemplars. The effectiveness of this recipe is limited by the ability to ret…
PredictionRetrievalSemantic ParsingSUBS: Subtree Substitution for Compositional Semantic Parsing
Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Several data augmentation methods have been pr…
Data AugmentationSemantic Parsing