Enhancing Frame Detection with Retrieval Augmented Generation

Recent advancements in Natural Language Processing have significantly improved the extraction of structured semantic representations from unstructured text, especially through Frame Semantic Role Labeling (FSRL). Despite this progress, the potential of Retrieval-Augmented Generation (RAG) models for frame detection remains under-explored. In this paper, we present the first RAG-based approach for frame detection called RCIF (Retrieve Candidates and Identify Frames). RCIF is also the first approach to operate without the need for explicit target span and comprises three main stages: (1) generation of frame embeddings from various representations ; (2) retrieval of candidate frames given an input text; and (3) identification of the most suitable frames. We conducted extensive experiments across multiple configurations, including zero-shot, few-shot, and fine-tuning settings. Our results show that our retrieval component significantly reduces the complexity of the task by narrowing the search space thus allowing the frame identifier to refine and complete the set of candidates. Our approach achieves state-of-the-art performance on FrameNet 1.5 and 1.7, demonstrating its robustness in scenarios where only raw text is provided. Furthermore, we leverage the structured representation obtained through this method as a proxy to enhance generalization across lexical variations in the task of translating natural language questions into SPARQL queries.

A Comprehensive Evaluation of the Copy Mechanism for Natural Language to SPARQL Query Generation

In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed a significant growth. Recently, the incorporation of the copy mechanism with traditional encoder-decoder architectures and the use of pre-trained encoder-decoders have set new performance benchmarks. This paper presents a large variety of experiments that replicate and expand upon recent NMT-based SPARQL generation studies, comparing pre-trained and non-pre-trained models, question annotation formats, and the use of a copy mechanism for non-pre-trained and pre-trained models. Our results show that either adding the copy mechanism or using a question annotation improves performances for nonpre-trained models and for pre-trained models, setting new baselines for three popular datasets.