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Computer Science > Computation and Language

arXiv:2502.12210 (cs)
[Submitted on 17 Feb 2025]

Title:Enhancing Frame Detection with Retrieval Augmented Generation

Authors:Papa Abdou Karim Karou Diallo, Amal Zouaq
View a PDF of the paper titled Enhancing Frame Detection with
Retrieval Augmented Generation, by Papa Abdou Karim Karou Diallo and
1 other authors
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    Abstract: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.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2502.12210 [cs.CL]
          (or arXiv:2502.12210v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2502.12210
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Papa Abdou Karim Karou Diallo [view email]
[v1] Mon, 17 Feb 2025 02:34:02 UTC (1,439 KB)
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