| Challenge: | Using the Transformer architecture, we trained similar systems across different tasks. |
| Approach: | They presented their results in the 6th Workshop on Asian Translation (WAT) translation task and their submissions to the task. |
| Outcome: | The proposed models perform better on tasks with smaller datasets and with smaller heads on multilingual datasets. |
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| 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)
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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. |
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. |
Idiap NMT System for WAT 2019 Multimodal Translation Task (D19-52)
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| Challenge: | In the past few decades, multi-modality has received critical attention in translation studies, although the benefit of visual modality in machine translation is still in debate. |
| Approach: | They propose to use the Transformer model and IITB English-Hindi parallel corpus as additional data sources for the evaluation and challenge test sets. |
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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. |
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. |
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NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System (D19-52)
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| 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. |
NICT’s participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT (D19-52)
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| 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. |
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task (D19-52)
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| Challenge: | Several data sets were provided for the task, but the data was limited and the distance and richness of the language pair were very challenging. |
| Approach: | They describe the SYSTRAN neural MT systems employed for the 6th Workshop on Asian Translation (WAT) they use the neural Transformer architecture learned over the provided resources and perform synthetic data generation experiments which aim at alleviating the data scarcity problem. |
| Outcome: | The proposed systems rank first according to automatic evaluations based on the neural Transformer architecture learned over the provided resources. |
SYSTRAN @ WNGT 2019: DGT Task (D19-56)
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| Challenge: | SYSTRAN participates in Document-level generation and trans-lation (DGT) task . data-to-text generation tasks are difficult because of the content selection and text generation data. |
| Approach: | They propose a Transformer-based datato-text generation model which jointly learns content selection and text generation. |
| Outcome: | The proposed model outperforms current state-of-the-art system on BLEU, content selection precision and content ordering metics. |