VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022

• URL: http://arxiv.org/abs/2308.07601v1
• Date: Tue, 15 Aug 2023 07:10:41 GMT
• Title: VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022
• Authors: Hai Long Trieu, Song Kiet Bui, Tan Minh Tran, Van Khanh Tran, Hai An Nguyen
• Abstract summary: We build our systems based on the neural-based Transformer model with the powerful multilingual denoising pre-trained model mBART. We achieve 38.9 BLEU on ChineseVietnamese and 38.0 BLEU on VietnameseChinese on the public test sets, which outperform several strong baselines.
• Score: 0.11249583407496218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present our systems participated in the VLSP 2022 machine translation shared task. In the shared task this year, we participated in both translation tasks, i.e., Chinese-Vietnamese and Vietnamese-Chinese translations. We build our systems based on the neural-based Transformer model with the powerful multilingual denoising pre-trained model mBART. The systems are enhanced by a sampling method for backtranslation, which leverage large scale available monolingual data. Additionally, several other methods are applied to improve the translation quality including ensembling and postprocessing. We achieve 38.9 BLEU on ChineseVietnamese and 38.0 BLEU on VietnameseChinese on the public test sets, which outperform several strong baselines.

Related papers

• An Effective Method using Phrase Mechanism in Neural Machine Translation [3.8979646385036166]
  We report an effective method using a phrase mechanism, PhraseTransformer, to improve the strong baseline model Transformer in constructing a Neural Machine Translation (NMT) system for parallel corpora Vietnamese-Chinese. Our experiments on the MT dataset of the VLSP 2022 competition achieved the BLEU score of 35.3 on Vietnamese to Chinese and 33.2 BLEU scores on Chinese to Vietnamese data.
  arXiv  Detail & Related papers  (2023-08-21T05:46:40Z)
• Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models [57.10972566048735]
  We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -- Helsinki NLP Spanish-English translation model. We experimented with 11 languages from America and report the setups we used as well as the results we achieved.
  arXiv  Detail & Related papers  (2023-05-27T08:10:40Z)
• Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task [54.63368889359441]
  This paper introduces WeChat's participation in WMT 2022 shared biomedical translation task on Chinese to English. Our systems are based on the Transformer, and use several different Transformer structures to improve the quality of translation. Our Chinese$to$English system, named Summer, achieves the highest BLEU score among all submissions.
  arXiv  Detail & Related papers  (2022-11-28T03:10:50Z)
• The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task [92.5087402621697]
  This paper describes the submission of our end-to-end YiTrans speech translation system for the IWSLT 2022 offline task. The YiTrans system is built on large-scale pre-trained encoder-decoder models. Our final submissions rank first on English-German and English-Chinese end-to-end systems in terms of the automatic evaluation metric.
  arXiv  Detail & Related papers  (2022-06-12T16:13:01Z)
• ViTA: Visual-Linguistic Translation by Aligning Object Tags [7.817598216459955]
  Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. We propose our system for the Multimodal Translation Task of WAT 2021 from English to Hindi.
  arXiv  Detail & Related papers  (2021-06-01T06:19:29Z)
• DiDi's Machine Translation System for WMT2020 [51.296629834996246]
  We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model. As a result, our submission achieves a BLEU score of $36.6$ in Chinese->English.
  arXiv  Detail & Related papers  (2020-10-16T06:25:48Z)
• SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task [111.91077204077817]
  We participated in four translation directions of three language pairs: English-Chinese, English-Polish, and German-Upper Sorbian. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.
  arXiv  Detail & Related papers  (2020-10-11T00:40:05Z)
• WeChat Neural Machine Translation Systems for WMT20 [61.03013964996131]
  Our system is based on the Transformer with effective variants and the DTMT architecture. In our experiments, we employ data selection, several synthetic data generation approaches, advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese to English system achieves 36.9 case-sensitive BLEU score, which is the highest among all submissions.
  arXiv  Detail & Related papers  (2020-10-01T08:15:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.