@inproceedings{ren-etal-2023-retrieve,
title = "Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation",
author = "Ren, Yubing and
Cao, Yanan and
Guo, Ping and
Fang, Fang and
Ma, Wei and
Lin, Zheng",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.17",
doi = "10.18653/v1/2023.acl-long.17",
pages = "293--306",
abstract = "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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%0 Conference Proceedings
%T Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation
%A Ren, Yubing
%A Cao, Yanan
%A Guo, Ping
%A Fang, Fang
%A Ma, Wei
%A Lin, Zheng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ren-etal-2023-retrieve
%X 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.
%R 10.18653/v1/2023.acl-long.17
%U https://aclanthology.org/2023.acl-long.17
%U https://doi.org/10.18653/v1/2023.acl-long.17
%P 293-306
Markdown (Informal)
[Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation](https://aclanthology.org/2023.acl-long.17) (Ren et al., ACL 2023)
ACL