@inproceedings{lee-etal-2023-nonparametric,
title = "Nonparametric Decoding for Generative Retrieval",
author = "Lee, Hyunji and
Kim, JaeYoung and
Chang, Hoyeon and
Oh, Hanseok and
Yang, Sohee and
Karpukhin, Vladimir and
Lu, Yi and
Seo, Minjoon",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.801",
doi = "10.18653/v1/2023.findings-acl.801",
pages = "12642--12661",
abstract = "The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed. To overcome the limitation, we propose Nonparametric Decoding (Np Decoding) which can be applied to existing generative retrieval models. Np Decoding uses nonparametric contextualized vocab embeddings (external memory) rather than vanilla vocab embeddings as decoder vocab embeddings. By leveraging the contextualized vocab embeddings, the generative retrieval model is able to utilize both the parametric and nonparametric space. Evaluation over 9 datasets (8 single-hop and 1 multi-hop) in the document retrieval task shows that applying Np Decoding to generative retrieval models significantly improves the performance. We also show that Np Decoding is data- and parameter-efficient, and shows high performance in the zero-shot setting.",
}
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<abstract>The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed. To overcome the limitation, we propose Nonparametric Decoding (Np Decoding) which can be applied to existing generative retrieval models. Np Decoding uses nonparametric contextualized vocab embeddings (external memory) rather than vanilla vocab embeddings as decoder vocab embeddings. By leveraging the contextualized vocab embeddings, the generative retrieval model is able to utilize both the parametric and nonparametric space. Evaluation over 9 datasets (8 single-hop and 1 multi-hop) in the document retrieval task shows that applying Np Decoding to generative retrieval models significantly improves the performance. We also show that Np Decoding is data- and parameter-efficient, and shows high performance in the zero-shot setting.</abstract>
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%0 Conference Proceedings
%T Nonparametric Decoding for Generative Retrieval
%A Lee, Hyunji
%A Kim, JaeYoung
%A Chang, Hoyeon
%A Oh, Hanseok
%A Yang, Sohee
%A Karpukhin, Vladimir
%A Lu, Yi
%A Seo, Minjoon
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lee-etal-2023-nonparametric
%X The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed. To overcome the limitation, we propose Nonparametric Decoding (Np Decoding) which can be applied to existing generative retrieval models. Np Decoding uses nonparametric contextualized vocab embeddings (external memory) rather than vanilla vocab embeddings as decoder vocab embeddings. By leveraging the contextualized vocab embeddings, the generative retrieval model is able to utilize both the parametric and nonparametric space. Evaluation over 9 datasets (8 single-hop and 1 multi-hop) in the document retrieval task shows that applying Np Decoding to generative retrieval models significantly improves the performance. We also show that Np Decoding is data- and parameter-efficient, and shows high performance in the zero-shot setting.
%R 10.18653/v1/2023.findings-acl.801
%U https://aclanthology.org/2023.findings-acl.801
%U https://doi.org/10.18653/v1/2023.findings-acl.801
%P 12642-12661
Markdown (Informal)
[Nonparametric Decoding for Generative Retrieval](https://aclanthology.org/2023.findings-acl.801) (Lee et al., Findings 2023)
ACL
- Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, and Minjoon Seo. 2023. Nonparametric Decoding for Generative Retrieval. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12642–12661, Toronto, Canada. Association for Computational Linguistics.