XNMT: The eXtensible Neural Machine Translation Toolkit
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Abstract (arXiv)
This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at https://github.com/neulab/xnmt
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2018.
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Citing papers per year: 2021: 6; 2022: 5; 2023: 3.
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- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?cited by 54
- 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
- 2023Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attackscited by 29
- 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
- 2021Shellcode_IA32: A Dataset for Automatic Shellcode Generationcited by 16
- 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
- 2022Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generatorscited by 10
- 2023Automating the Correctness Assessment of AI-generated Code for Security Contextscited by 6
- 2021Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Modelscited by 5
- 2021Understanding the Impact of UGC Specificities on Translation Qualitycited by 3
- 2022Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Datacited by 3
- 2023Enhancing Robustness of AI Offensive Code Generators via Data Augmentationcited by 3
- 2023Automating the Correctness Assessment of AI-generated Code for Security Contexts28 Oct 2023
- 2023Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks4 Aug 2023
- 2023Enhancing Robustness of AI Offensive Code Generators via Data Augmentation8 Jun 2023
- 2022Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data20 Dec 2022
- 2022Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators12 Dec 2022
- 2022Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation29 Mar 2022
- 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation18 Mar 2022
- 2022Can We Generate Shellcodes via Natural Language? An Empirical Study8 Feb 2022
- 2021Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models24 Oct 2021
- 2021Understanding the Impact of UGC Specificities on Translation Quality24 Oct 2021
- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?2 Jun 2021
- 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation28 May 2021
2 more
- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?cited by 54
- 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
- 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
- 2021Shellcode_IA32: A Dataset for Automatic Shellcode Generationcited by 16
- 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translationcited by 11
- 2022Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generatorscited by 10
- 2023Automating the Correctness Assessment of AI-generated Code for Security Contextscited by 6
- 2021Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Modelscited by 5
- 2021Understanding the Impact of UGC Specificities on Translation Qualitycited by 3
- 2022Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Datacited by 3
- 2023Enhancing Robustness of AI Offensive Code Generators via Data Augmentationcited by 3
- 2022Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generationcited by 2
12 most cited of 13 All 13 (JSON)
- 2023Automating the Correctness Assessment of AI-generated Code for Security Contexts28 Oct 2023
- 2023Enhancing Robustness of AI Offensive Code Generators via Data Augmentation8 Jun 2023
- 2022Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data20 Dec 2022
- 2022Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators12 Dec 2022
- 2022Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation29 Mar 2022
- 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation18 Mar 2022
- 2022Can We Generate Shellcodes via Natural Language? An Empirical Study8 Feb 2022
- 2021Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models24 Oct 2021
- 2021Understanding the Impact of UGC Specificities on Translation Quality24 Oct 2021
- 2021Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?2 Jun 2021
- 2021How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation28 May 2021
- 2021Shellcode_IA32: A Dataset for Automatic Shellcode Generation27 Apr 2021
12 newest of 13 All 13 (JSON)