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.

Similar Papers

NTT Neural Machine Translation Systems at WAT 2019 (D19-52)

Copied to clipboard

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.
Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT (2020.lrec-1)

Copied to clipboard

Challenge: Existing methods to train NMT systems with noisy data are not sufficient . a recent increase in foreigners visiting Japan has created a significant information gap .
Approach: They propose a Japanese-English parallel news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus .
Outcome: The proposed corpus improves translation quality and is more effective than existing methods.
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019 (D19-52)

Copied to clipboard

Challenge: Using parallel corpora of different language pairs as training data is effective for multilingual neural machine translation model in extremely low resource situations.
Approach: They propose to use Japanese-English and English-Russian parallel corpora as training data for their system to improve JapaneseRussian news translation.
Outcome: The proposed system improves translation quality for JapaneseRussian language pairs in low resource situations.
Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)

Copied to clipboard

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.
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)

Copied to clipboard

Challenge: Existing scientific corpus for English-Persian language pairs is lacking . supervised neural machine translation requires millions of parallel sentences .
Approach: They propose a parallel corpus called Esposito which contains 3.5 million parallel sentences . they also propose 'test sets' that might serve as a baseline for future studies .
Outcome: The proposed system improves the baseline on average by 7.6 and 8.4 BLEU scores for English-Persian language pairs.
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)

Copied to clipboard

Challenge: In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low.
Approach: They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs .
Outcome: The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs .
Effective Use of Target-side Context for Neural Machine Translation (2020.coling-main)

Copied to clipboard

Challenge: Existing methods to train NMT systems with noisy data are not sufficient . et al., 2018) found that NMT models can learn with multiple types of corpora .
Approach: They propose a Japanese-English news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus .
Outcome: The proposed corpus improves translation quality and is more efficient than existing methods.
UCSYNLP-Lab Machine Translation Systems for WAT 2019 (D19-52)

Copied to clipboard

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.
Overview of the 6th Workshop on Asian Translation (D19-52)

Copied to clipboard

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.
CVIT’s submissions to WAT-2019 (D19-52)

Copied to clipboard

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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations