@inproceedings{ye-etal-2024-tram,
title = "Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization",
author = "Ye, Tong and
Wu, Lingfei and
Ma, Tengfei and
Zhang, Xuhong and
Du, Yangkai and
Liu, Peiyu and
Ji, Shouling and
Wang, Wenhai",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.186",
doi = "10.18653/v1/2024.findings-naacl.186",
pages = "2959--2971",
abstract = "Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are limited by their inability to access external knowledge. To address this limitation, an emerging trend is combining neural models with external knowledge through retrieval methods. Previous methods have relied on the sentence-level retrieval paradigm on the encoder side. However, this paradigm is coarse-grained, noise-filled and cannot directly take advantage of the high-quality retrieved summary tokens on the decoder side. In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries. Furthermore, to overcome the challenge of token-level retrieval in capturing contextual code semantics, we also propose integrating code semantics into individual summary tokens. The results of extensive experiments and human evaluation show that our token-level retrieval-augmented approach significantly improves performance and is more interpretable.",
}
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<abstract>Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are limited by their inability to access external knowledge. To address this limitation, an emerging trend is combining neural models with external knowledge through retrieval methods. Previous methods have relied on the sentence-level retrieval paradigm on the encoder side. However, this paradigm is coarse-grained, noise-filled and cannot directly take advantage of the high-quality retrieved summary tokens on the decoder side. In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries. Furthermore, to overcome the challenge of token-level retrieval in capturing contextual code semantics, we also propose integrating code semantics into individual summary tokens. The results of extensive experiments and human evaluation show that our token-level retrieval-augmented approach significantly improves performance and is more interpretable.</abstract>
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%0 Conference Proceedings
%T Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
%A Ye, Tong
%A Wu, Lingfei
%A Ma, Tengfei
%A Zhang, Xuhong
%A Du, Yangkai
%A Liu, Peiyu
%A Ji, Shouling
%A Wang, Wenhai
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ye-etal-2024-tram
%X Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significant performance in this field, they are limited by their inability to access external knowledge. To address this limitation, an emerging trend is combining neural models with external knowledge through retrieval methods. Previous methods have relied on the sentence-level retrieval paradigm on the encoder side. However, this paradigm is coarse-grained, noise-filled and cannot directly take advantage of the high-quality retrieved summary tokens on the decoder side. In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries. Furthermore, to overcome the challenge of token-level retrieval in capturing contextual code semantics, we also propose integrating code semantics into individual summary tokens. The results of extensive experiments and human evaluation show that our token-level retrieval-augmented approach significantly improves performance and is more interpretable.
%R 10.18653/v1/2024.findings-naacl.186
%U https://aclanthology.org/2024.findings-naacl.186
%U https://doi.org/10.18653/v1/2024.findings-naacl.186
%P 2959-2971
Markdown (Informal)
[Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization](https://aclanthology.org/2024.findings-naacl.186) (Ye et al., Findings 2024)
ACL
- Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, and Wenhai Wang. 2024. Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2959–2971, Mexico City, Mexico. Association for Computational Linguistics.