Research Article
Open access
Published on 20 February 2023
Download pdf
Huang,T.;Jia,Y.;Pang,H.;Sun,Z. (2023).Neural Machine Translation in Translation and Program Repair.Theoretical and Natural Science,2,194-203.
Export citation

Neural Machine Translation in Translation and Program Repair

Tingsong Huang *,1, Yifei Jia 2, Haohua Pang 3, Zhe Sun 4
  • 1 College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, 350108, China
  • 2 Jinan-Birmingham Joint Institute, Jinan University, Guangzhou, Guangdong, 511436, China
  • 3 Division of Science and Technology, Beijing Normal University - Hong Kong Baptist University United International College, Zhuhai, Guangdong, 519087, China
  • 4 School of Business and Management, Jilin University, Changchun, Jilin, 130015, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2/20220143

Abstract

Translation is a challenge for humans since it needs a good command of two or more languages. When it comes to computer programs, it is even more complex as it is difficult for computers to imitate human translators. With the emergence of deep learning algorithms, especially neural network architectures, neural machine translation (NMT) models gradually outperformed previous machine translation models and became the new mainstream in practical machine translation (MT) systems. Nowadays, NMT has been developing for several years and has been applied in many fields. This paper is focused on studies on four different application categories of NMT models: 1) Text NMT; 2) Automatic program repair (based on NMT); 3) Simultaneous translation. Our work provides a summary of the latest research on different applications of NMT and makes comments on their development in the future. This paper also mentioned the shortcomings of existing studies in this essay and pointed out some possible research directions.

Keywords

automatic program repair, text NMT, neural machine translation

[1]. Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate[J]. Computer Science, 2014.

[2]. Cho K, Merrienboer B V, Bahdanau D, et al. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches[J]. Computer Science, 2014.

[3]. Luong M T, Pham H, Manning C D. Effective Approaches to Attention-based Neural Machine Translation[J]. Computer Science, 2015.

[4]. Chung J, Cho K, Bengio Y. A Character-level Decoder without Explicit Segmentation for Neural Machine Translation[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[5]. Luong M T, Manning C D. Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[6]. Costa-Jussà, Marta R, Fonollosa J. Character-based Neural Machine Translation[J]. arXiv preprint arXiv:1511.04586. 2016.

[7]. Sennrich R, Haddow B, Birch A. Neural Machine Translation of Rare Words with Subword Units[J]. Computer Science, 2015.

[8]. Wang L, Tu Z, Way A, et al. Exploiting Cross-Sentence Context for Neural Machine Translation[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017.

[9]. Bawden R, Sennrich R, Birch A, et al. Evaluating Discourse Phenomena in Neural Machine Translation[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

[10]. Rios A, Mascarell L,Rico Sennrich. Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings[C]// Conference on Machine Translation. 2017.

[11]. Voita E, Sennrich R, Titov I. When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion[C]// 2019.

[12]. Guo M, Shen Q, Yang Y, et al. Effective Parallel Corpus Mining using Bilingual Sentence Embeddings[C]// Proceedings of the Third Conference on Machine Translation: Research Papers. 2018.

[13]. Lai G, Dai Z, Yang Y. Unsupervised Parallel Corpus Mining on Web Data[J]. 2020.

[14]. Sennrich R, Haddow B, Birch A. Improving Neural Machine Translation Models with Monolingual Data[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[15]. Artetxe M, Labaka G, Casas N, et al. Do all roads lead to Rome? Understanding the role of initialization in iterative back-translation[J]. Knowledge-Based Systems, 2020.

[16]. Ranathunga S, Lee E, Skenduli M P, et al. Neural Machine Translation for Low-Resource Languages: A Survey[J]. arXiv preprint arXiv:2106.15115. 2021.

[17]. Xia Y, He D, Qin T, et al. Dual Learning for Machine Translation[C]// Advances in neural information processing systems. 2016.

[18]. Pan S J, Qiang Y. A Survey on Transfer Learning[J]. 2009.

[19]. Wang R, Tan X, Luo R, et al. A Survey on Low-Resource Neural Machine Translation[C]// 2021.

[20]. Ming W, Chen J, Wu R, et al. Context-Aware Patch Generation for Better Automated Program Repair[C]// the 40th International Conference. IEEE Computer Society, 2018.

[21]. Chen Z, Kommrusch S J, Tufano M, et al. SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair[J]. IEEE Transactions on Software Engineering, 2019, PP (99):1-1.

[22]. Lutellier T, Pham H V, Pang L, et al. CoCoNuT: combining context-aware neural translation models using ensemble for program repair[C]// ISSTA '20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 2020.

[23]. Jiang N, Letelier T, Tan L. CURE: Code-Aware Neural Machine Translation for Automatic Program Repair[C]// 2021.

[24]. Lee Y H, Shin J H, Kim Y K. Simultaneous neural machine translation with a reinforced attention mechanism[J]. ETRI Journal. 2021.

[25]. Cho K, Esipova M. Can neural machine translation do simultaneous translation? [J]. arXiv preprint arXiv:1610.00388. 2016.

[26]. Dalvi F, Durrani N, Sajjad H, et al. Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation[C]// North American Chapter of the Association of Computational Linguistics: Human Language Technologies. 2018.

[27]. Gu J, Neubig G, Cho K, et al. Learning to Translate in Real-time with Neural Machine Translation[J]. arXiv preprint arXiv:1610.00388. 2017.

[28]. Alinejad A, Siahbani M, Sarkar A. Prediction Improves Simultaneous Neural Machine Translation[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

[29]. Arivazhagan N, Cherry C, Macherey W, et al. Monotonic Infinite Lookback Attention for Simultaneous Machine Translation[C]// Meeting of the Association for Computational Linguistics. 2019.

[30]. Chousa K, Sudoh K, Nakamura S. Simultaneous Neural Machine Translation using Connectionist Temporal Classification[J]. arXiv preprint arXiv:1911.11933. 2019.

Cite this article

Huang,T.;Jia,Y.;Pang,H.;Sun,Z. (2023).Neural Machine Translation in Translation and Program Repair.Theoretical and Natural Science,2,194-203.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content

About volume

Volume title: Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)

Conference website: https://www.confciap.org/
ISBN:978-1-915371-13-3(Print) / 978-1-915371-14-0(Online)
Conference date: 4 August 2022
Editor:Michael Harre, Marwan Omar, Roman Bauer
Series: Theoretical and Natural Science
Volume number: Vol.2
ISSN:2753-8818(Print) / 2753-8826(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).