Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge (2025)

Abstract: Open-domain semantic parsing remains a challenging task, as neural 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 task and introduce Retrieval-Augmented Semantic Parsing (RASP), a simple yet effective approach that integrates external symbolic knowledge into the parsing process. Our experiments not only show that LLMs outperform previous encoder-decoder baselines for semantic parsing, but that RASP further enhances their ability to predict unseen concepts, nearly doubling the performance of previous models on out-of-distribution concepts. These findings highlight the promise of leveraging large language models and retrieval mechanisms for robust and open-domain semantic parsing.

## introduction open-domain semantic parsing involves mapping natural language text to formal meaning representations, capturing the concepts, relations between them, and the contexts in which they appear @xcite @xcite @xcite . such meaning representations are applied in many downstream applications-ranging from database querying to embodied question answering-where parsers must handle a vast array of concepts that may not appear in the training data. while neural encoder-decoder architectures have shown impressive performance in semantic parsing tasks, their reliance on training distributions constrains their ability to generalize, especially to out-of-distribution (ood) concepts. most existing semantic parsers struggle to interpret the symbols, such as rare senses, often defaulting the unseen words to the most frequent meaning encountered during training. as a result, they fail to adapt to novel linguistic phenomena and remain limited to fixed patterns. recent work @xcite have attempted to mitigate these limitations by encoding concept representations symbolically, forcing models to learn underlying structural knowledge from resources like wordnet @xcite . however, these approaches require substantial preprocessing and intricate encodings that can be difficult for models to fully exploit. in our work, instead, we explore the potential of large language models, powerful decoder-only architectures with strong in-context learning capabilities and extensive pretraining, to enhance the ability of semantic parsers to generalize. we pose two central research questions: in section 2 we provide background on the semantic formalism of our choice, earlier approach to semantic parsing, and the challenge of an important task, word sense disambiguation. then we propose retrieval-augmented semantic parsing (rasp) in section 3, a technique that integrates a retrieval mechanism into parsing. rasp leverages external lexical knowledge in the input, enabling the model to dynamically access and interpret relevant concept information. by incorporating this retrieval step (section 4), we relax the reliance on lemmabased mappings and allow the model to adapt more naturally to unseen words or senses. our results show that this approach nearly doubles the performance on predicting ood concepts compared to previous methods, demonstrating a substantial advancement in handling challenging open-domain data (section 5). ## retrieval-augmented semantic parsing we propose a new method that combines retrievalaugmented generation with semantic interpretation: retrieval-augmented semantic parsing (rasp), a framework that is outlined in figure 2 . it comprises two key components: retrieval and parsing. different from the dense passage retrieval @xcite ) method, which is commonly employed in question-answering tasks, our retrieval process is designed to be more straightforward and tailored to the needs of semantic parsing. the process begins with tokenizing and lemmatizing foot_0 the source text. following these, we perform a search for relevant concept synsets in an external knowledge base, specifically wordnet. for example, in the sentence "mary went for birdwatching. she saw a harrier, a golden eagle, and a hobby", the retrieval process would identify multiple synsets for "go", "birdwatch", "see", "harrier", "golden eagle" and "hobby", as illustrated in ## conclusion this paper demonstrates that llms, even without retrieval augmentation, outperform previous encoder-decoder approaches in semantic parsing for discourse representation structures, thereby answering our first research question in the affirmative, setting a new state of the art. we also show that our proposed retrieval-augmented semantic parsing (rasp) framework, which integrates external lexical knowledge, further enhances the performance of llms. notably, rasp nearly doubles the accuracy on out-of-distribution concepts, which answers our second research question and confirms robust generalization ability of rasp in open-domain scenarios. our experiments show that by simply appending relevant information to the model input, the rasp approach offers a practical and intuitive approach that can be easily applied to other meaning representations used in natural language processing, such as amr @xcite and bmr @xcite .