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Bi-directional generative retrieval-augmented diffusion models for document-level informative argument extraction
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AI
DOI：10.1016/j.neucom.2025.131360.png)
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Abstract
En 中文
Document-level Informative Argument Extraction (IAE) presents a significant challenge in the field of information extraction. This challenge stems from the necessity for implicit coreference reasoning and the linking of long-range dependencies between events within a document. Despite recent efforts to leverage generation-based document-level extraction to enhance cross-sentence inference capabilities and capture more interactions between different events, these methods often fall short in their generation quality due to difficulties in understanding the global context. Motivated by these observations and the high-quality generation results of recent diffusion models, we propose an effective model known as BGRD (Bi-directional Generative Retrieval-augmented Diffusion models) for document-level IAE. In BGRD, a text diffusion model is designed to generate high-quality target event sequences that mutually benefit the retrieval stage, leveraging previously generated events as a retrieval source. Firstly, a bi-directional retrieval mechanism is investigated to refine the denoising process, effectively exploring the knowledge from retrieved samples. This enhances the text diffusion model’s ability to capture the global context interconnecting the events. Secondly, retrieval-augmented cross-attention is employed between the retrieved samples and the target event sequences (random Gaussian noise during the inference phase) within the text diffusion model. Through this interaction, the quality of the retrieval source is improved by generating highly informative event sequences, which benefits the bi-directional retrieval stage. Extensive experiments on the publicly available argument extraction datasets demonstrate the superiority of our proposed BGRD model over existing approaches.
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6.5
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2.5W
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6.5W
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