Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019

Imankulova, Aizhan; Kaneko, Masahiro; Komachi, Mamoru

doi:10.18653/v1/d19-5221

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Motivated by this inter-disciplinary research work, the future scope of this study from different perspectives is as follows, Machine translation perspective : One direction is to try to build MT systems using more data, both in-domain and out-of-domain data. Furthermore, other domain adaptation methods, (Imankulova et al, 2019 ; Pham, Crego, and Yvon, 2021), are possible which can be investigated in future work. Domain expert perspective : Cyberbullying in pre-adolescent discourse despite recent computational advances is still quite under-researched. Scrutinizing other OSN discourse platforms to create more resources for cyberbullying detection that can aid in creating plausible OSN monitoring and intervention policies can be a significant contribution to this domain. ML/DL classification perspective : Due to the sparse and scarce nature of the publicly available cyberbullying resources, recent advances in DL have been focused only on the binary classification or detection of cyberbullying.…”

Section: Discussionmentioning

confidence: 99%

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Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Verma

Popović²,

Poulis³

et al. 2022

Nat. Lang. Eng.

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Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.

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Section: Discussionmentioning

confidence: 99%

“…Machine translation perspective : One direction is to try to build MT systems using more data, both in-domain and out-of-domain data. Furthermore, other domain adaptation methods, (Imankulova et al, 2019 ; Pham, Crego, and Yvon, 2021), are possible which can be investigated in future work.…”

Section: Discussionmentioning

confidence: 99%

Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Verma

Popović²,

Poulis³

et al. 2022

Nat. Lang. Eng.

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“…Based on this, from the multimodal translation perspective, the visual narrative analysis theory and the auditory grammar framework are integrated into translation research [11][12][13]. A multimodal translation analysis framework integrating visual and aural modes should be constructed to analyze the effects of visual modes, auditory modes, and modal interactions on translation in Russian texts, with a view to breaking through the shortcomings of previous Russian translations that focused only on the single modality of text [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effective application of multimodal discourse analysis in Russian translation

Wu,

Zhang,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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Based on ELAN multimodal discourse analysis software, this paper constructs a multimodal Russian translation model based on the machine translation model with visual grammar and multimodal discourse analysis as the theoretical basis. To address the issue of missing semantics caused by insufficient input information at the source of real-time translation, the model uses images as auxiliary modalities. The real-time Russian translation model is constructed using the wait-k strategy and the concept of multimodal self-attention. Experiments and analysis are carried out on the Multi30k training set, and the generalization ability and translation effect of the model are finally evaluated with the test set. The results show that by applying multimodal discourse analysis to Russian translation, the three translation evaluation indexes of BLEU, METEOR, and TER are improved by 1.3, 1.0, and 1.4 percentage points, respectively, and the phenomenon of phantom translation is effectively reduced.

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Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019

Cited by 2 publications

References 8 publications

Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Effective application of multimodal discourse analysis in Russian translation

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