@inproceedings{zhu-etal-2024-knn,
title = "k{NN}-{BOX}: A Unified Framework for Nearest Neighbor Generation",
author = "Zhu, Wenhao and
Zhao, Qianfeng and
Lv, Yunzhe and
Huang, Shujian and
Zhao, Siheng and
Liu, Sizhe and
Chen, Jiajun",
editor = "Aletras, Nikolaos and
De Clercq, Orphee",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-demo.2",
pages = "10--17",
abstract = "Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and visualization for this novel paradigm. kNN-BOX decomposes the datastore-augmentation approach into three modules: datastore, retriever and combiner, thus putting diverse kNN generation methods into a unified way. Currently, kNN-BOX has provided implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. It is easy for users to reproduce these existing work or customize their own models. Besides, users can interact with their kNN generation systems with kNN-BOX to better understand the underlying inference process in a visualized way. In experiment section, we apply kNN-BOX for machine translation and three other seq2seq generation tasks (text simplification, paraphrase generation and question generation). Experiment results show that augmenting the base neural model with kNN-BOX can bring large performance improvement in all these tasks. The code and document of kNN-BOX is available at https://github.com/NJUNLP/knn-box. The demo can be accessed at http://nlp.nju.edu.cn/demo/knn-box/. The introduction video is available at https://www.youtube.com/watch?v=m0eJldHVR3w.",
}
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<abstract>Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and visualization for this novel paradigm. kNN-BOX decomposes the datastore-augmentation approach into three modules: datastore, retriever and combiner, thus putting diverse kNN generation methods into a unified way. Currently, kNN-BOX has provided implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. It is easy for users to reproduce these existing work or customize their own models. Besides, users can interact with their kNN generation systems with kNN-BOX to better understand the underlying inference process in a visualized way. In experiment section, we apply kNN-BOX for machine translation and three other seq2seq generation tasks (text simplification, paraphrase generation and question generation). Experiment results show that augmenting the base neural model with kNN-BOX can bring large performance improvement in all these tasks. The code and document of kNN-BOX is available at https://github.com/NJUNLP/knn-box. The demo can be accessed at http://nlp.nju.edu.cn/demo/knn-box/. The introduction video is available at https://www.youtube.com/watch?v=m0eJldHVR3w.</abstract>
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%0 Conference Proceedings
%T kNN-BOX: A Unified Framework for Nearest Neighbor Generation
%A Zhu, Wenhao
%A Zhao, Qianfeng
%A Lv, Yunzhe
%A Huang, Shujian
%A Zhao, Siheng
%A Liu, Sizhe
%A Chen, Jiajun
%Y Aletras, Nikolaos
%Y De Clercq, Orphee
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F zhu-etal-2024-knn
%X Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick development and visualization for this novel paradigm. kNN-BOX decomposes the datastore-augmentation approach into three modules: datastore, retriever and combiner, thus putting diverse kNN generation methods into a unified way. Currently, kNN-BOX has provided implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. It is easy for users to reproduce these existing work or customize their own models. Besides, users can interact with their kNN generation systems with kNN-BOX to better understand the underlying inference process in a visualized way. In experiment section, we apply kNN-BOX for machine translation and three other seq2seq generation tasks (text simplification, paraphrase generation and question generation). Experiment results show that augmenting the base neural model with kNN-BOX can bring large performance improvement in all these tasks. The code and document of kNN-BOX is available at https://github.com/NJUNLP/knn-box. The demo can be accessed at http://nlp.nju.edu.cn/demo/knn-box/. The introduction video is available at https://www.youtube.com/watch?v=m0eJldHVR3w.
%U https://aclanthology.org/2024.eacl-demo.2
%P 10-17
Markdown (Informal)
[kNN-BOX: A Unified Framework for Nearest Neighbor Generation](https://aclanthology.org/2024.eacl-demo.2) (Zhu et al., EACL 2024)
ACL
- Wenhao Zhu, Qianfeng Zhao, Yunzhe Lv, Shujian Huang, Siheng Zhao, Sizhe Liu, and Jiajun Chen. 2024. kNN-BOX: A Unified Framework for Nearest Neighbor Generation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 10–17, St. Julians, Malta. Association for Computational Linguistics.