Domain Transfer based Data Augmentation for Neural Query Translation

Liang, Yao; Yang, Baosong; Zhang, Haibo; Chen, Boxing; Luo, Weihua

doi:10.18653/v1/2020.coling-main.399

“…It is a promising direction to explore other behavior in future, such as clickthrough and editing operations. Moreover, following recent advancements in domain adaptation for NMT, we plan to further improve our model via adversial training based knowledge transfer (Zeng et al, 2018;Yao et al, 2020;Su et al, 2021) and dual knowledge transfer (Zeng et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Towards User-Driven Neural Machine Translation

Lin¹,

Liang²,

Yang³

et al. 2021

Preprint

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A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of userbehavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations. 1

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“…Personalized Machine Translation Recently, some researchers have employed domain adaptation (Zhang et al, 2019;Gururangan et al, 2020;Yao et al, 2020) to generate personalized translations. For example, Mirkin et al (2015) show that the translation generated by the SMT model has an adverse effect on the prediction of author personalities, demonstrating the necessity of personalized machine translation.…”

Section: Related Workmentioning

confidence: 99%

Towards User-Driven Neural Machine Translation

Lin¹,

Liang²,

Yang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

Self Cite

View full text Add to dashboard Cite

A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior (e.g., historical inputs). However, current NMT systems marginally consider the user behavior due to: 1) the difficulty of modeling user portraits in zero-shot scenarios, and 2) the lack of userbehavior annotated parallel dataset. To fill this gap, we introduce a novel framework called user-driven NMT. Specifically, a cache-based module and a user-driven contrastive learning method are proposed to offer NMT the ability to capture potential user traits from their historical inputs under a zero-shot learning fashion. Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus. Experimental results confirm that the proposed user-driven NMT can generate user-specific translations. 1

show abstract

“…[79] Sampling Rules [26], [28], [68], [81], [100], [105] [80], [106], [107] [108] Seq2Seq [28], [29], [86], [109] [13], [85], [110], [111], [84] [86]…”

Section: Textunclassified

Data Augmentation Approaches in Natural Language Processing: A Survey

Li,

Hou,

Che

2021

Preprint

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As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data. In this survey, we frame DA methods into three categories based on the diversity of augmented data, including paraphrasing, noising, and sampling. Our paper sets out to analyze DA methods in detail according to the above categories. Further, we also introduce their applications in NLP tasks as well as the challenges.

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Domain Transfer based Data Augmentation for Neural Query Translation

Cited by 12 publications

References 28 publications

Towards User-Driven Neural Machine Translation

Towards User-Driven Neural Machine Translation

Towards User-Driven Neural Machine Translation

Data Augmentation Approaches in Natural Language Processing: A Survey

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