| Challenge: | We submitted two systems for scientific paper subtask and timely disclosure subtask . we evaluated the usefulness of incorporating external data from a wide variety of web pages to improve the translation quality. |
| Approach: | They describe two different translation tasks submitted to WAT 2019 . they submitted scientific paper subtasks and timely disclosure subtask . |
| Outcome: | The proposed system performed better on scientific paper and timely disclosure subtasks. |
Similar Papers
Our Neural Machine Translation Systems for WAT 2019 (D19-52)
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| Challenge: | In the last five years, statistical machine translation is gradually fading out in favor of neural machine translation. |
| Approach: | They describe a novel Neural Machine Translation (NMT) system for the WAT 2019 translation tasks they focus on. |
| Outcome: | The proposed system improves translation accuracy while replacing absolute position representations with relative positions. |
UCSYNLP-Lab Machine Translation Systems for WAT 2019 (D19-52)
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| Challenge: | Neural machine translation (NMT) has achieved stateof-the-art performance on various language pairs. |
| Approach: | They describe the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both directions. |
| Outcome: | The proposed translation system improves the performance of Myanmar-English translation tasks. |
English-Myanmar Supervised and Unsupervised NMT: NICT’s Machine Translation Systems at WAT-2019 (D19-52)
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| Challenge: | NICT participated in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (My) - English task in both translation directions. |
| Approach: | They present the participation of the NICT in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions. |
| Outcome: | The proposed systems perform the third in English-to-Myanmar and the second in Myanmar-to English according to BLEU score. |
CVIT’s submissions to WAT-2019 (D19-52)
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| Challenge: | In this paper, we explore multiway-models for Indian languages. |
| Approach: | They propose to use a Transformer architecture to experiment with multilingual models and methods for low-resource languages. |
| Outcome: | The proposed system is feasible in low-resource languages. |
Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
| Approach: | They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality. |
| Outcome: | The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus. |
Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at WAT 2019 (D19-52)
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| Challenge: | In addition to the JIJI Corpus, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content- equivalently. |
| Approach: | They propose to use JIJI Corpus and Equivalent-style sentences to translate Japanese news sentences into English content- equivalently. |
| Outcome: | The proposed translation models achieved the best human evaluation scores in the newswire translation tasks at WAT 2019 . they used the JIJI Corpus, which was provided by the task organizer, and the Equivalent-style translation model to translate Japanese news sentences into English content- equivalently. |
LTRC-MT Simple & Effective Hindi-English Neural Machine Translation Systems at WAT 2019 (D19-52)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach for low resource languages. |
| Approach: | They propose to use both Recurrent Neural Networks & Transformer architectures to train NMT models. |
| Outcome: | The proposed model outperforms Statistical Machine Translation (SMT) techniques on a low resource Hindi-English language pair. |
Combining Translation Memory with Neural Machine Translation (D19-52)
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| Challenge: | Existing systems that combine translation memory and statistical machine translation (MT) models are able to translate less familiar phrases and sentences without sacrificing quality. |
| Approach: | They propose to combine translation memory and Neural Machine Translation (NMT) models to select final translation outputs when similarity score of a test source sentence exceeds the predefined threshold. |
| Outcome: | The proposed system significantly improves translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. |
Overview of the 6th Workshop on Asian Translation (D19-52)
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Toshiaki Nakazawa, Nobushige Doi, Shohei Higashiyama, Chenchen Ding, Raj Dabre, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Yusuke Oda, Shantipriya Parida, Ondřej Bojar, Sadao Kurohashi
| Challenge: | The 6th workshop on Asian translation (WAT2019) was held in hong kong, hongkong, and hong kong. |
| Approach: | They present the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) 25 teams participated in the shared task and 10 research paper submissions were accepted . |
| Outcome: | The results of the 6th workshop on Asian translation (WAT2019) include JaEn, JaZh scientific paper translation subtasks, Ja'En, ja'Ko, Ja’En patent translation sub tasks, Hi'En and My'En patent subtask and Ru'Ja news commentary translation task. |
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (D19-52)
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Benjamin Marie, Hour Kaing, Aye Myat Mon, Chenchen Ding, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
| Challenge: | Using cleaned and normalized noisy monolingual data, supervised neural and statistical machine translation systems performed among the best for the four translation directions. |
| Approach: | They present supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. |
| Outcome: | The proposed systems performed among the best for the four translation directions. |