Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation
Open accessAbstract
Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks.These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation.These methods use similarity-based retrieval, which is based on a simple hypothesis: the more the retrieved demonstration resembles the original input, the more likely the demonstration label resembles the input label.However, due to the complexity of event labels and sparsity of event arguments, this hypothesis does not always hold in document-level EAE.This raises an interesting question: How do we design the retrieval strategy for document-level EAE?We investigate various retrieval settings from the input and label distribution views in this paper.We further augment document-level EAE with pseudo demonstrations sampled from event semantic regions that can cover adequate alternatives in the same context and event schema.Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods and analyze why they work.
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REPLUG utilizes a retriever and an LLM to calculate the probability distributions of the retrieved documents and then performs supervised training by computing the KL divergence.
“REPLUG [72] utilizes a retriever and an LLM to calculate the probability distributions of the retrieved documents and then performs supervised training by computing the KL divergence.”
From Retrieval-Augmented Generation for Large Language Models: A Survey (2023) · cites this paper as [72]
The Introduction and §2.3 describe this paper’s retrieval-augmented event argument extraction method and its negative-log-likelihood training objective. Searches for REPLUG, retrieved-document probability distributions, and KL divergence found no passage addressing how REPLUG is trained. The different objective used by this paper does not establish a contrary claim about REPLUG. (LLM, conf=0.76)
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- Authors
- Ren, Y., Cao, Y., Guo, P., Fang, F., Ma, W., & Lin, Z.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Ren, Yubing
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 293-306
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385565231
- Cited by
- 42
- References
- 19
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 12 · 2023: 2
- FWCI
- 7.44
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Chinese Academy of Sciences (XDC02030400)
- National Key Research and Development Program of China (2022YFB3102200)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- retrieval augmentation, label sparsity, rams
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