| Challenge: | UCSMNLP submitted to WAT 2019 Translation Tasks focusing on Myanmar-English translation. |
| Approach: | They propose to use Name Entity Recognition corpus and bilingual dictionary to build phrase based statistical machine translation system using listwise reranking process and initial distortion weight is changed to improve translation quality. |
| Outcome: | The proposed system outperforms the baseline system in the Myanmar-English translation task. |
Similar Papers
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. |
English-Myanmar Supervised and Unsupervised NMT: NICT’s Machine Translation Systems at WAT-2019 (D19-52)
Copied to clipboard
| 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. |
Our Neural Machine Translation Systems for WAT 2019 (D19-52)
Copied to clipboard
| 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. |
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English (D19-52)
Copied to clipboard
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. |
NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System (D19-52)
Copied to clipboard
| Challenge: | a majority of Asians speak low to medium resource languages . lack of resources poses a challenge, which requires innovative solutions . |
| Approach: | They propose a Neural Machine Translation system for Tamil-English Indic Task . they train a system for both Tamil-to-English and English-to Tamil pairs . |
| Outcome: | The proposed system is based on a Transformer-based architecture and is not very innovative, but can be treated as an incremental step in this direction. |
Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)
Copied to clipboard
Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
| Challenge: | Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling. |
| Approach: | They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results. |
| Outcome: | The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher. |
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. |
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. |
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. |
NICT’s participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT (D19-52)
Copied to clipboard
| Challenge: | In this paper, we describe our submissions for the following tasks: English–Tamil translation and Russian–Japanese translation. |
| Approach: | They propose to use multilingual domain adaptation and back-translation to improve translations in Russian–Japanese and English–Tamil. |
| Outcome: | The proposed techniques perform better in Russian–Japanese and English–Tamil translation tasks. |