KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
Copied to clipboard
Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
Similar Papers
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. |
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. |
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. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |
Combining Translation Memory with Neural Machine Translation (D19-52)
Copied to clipboard
| 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. |
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. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
Copied to clipboard
| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (2021.acl-long)
Copied to clipboard
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum
| Challenge: | Autoregressive neural machine translation (NMT) uses a tractable likelihood computation and efficient sampling. |
| Approach: | They propose to use an energy-based model to mimic the behavior of the task measure and use it to train an energy based re-ranking algorithm. |
| Outcome: | The proposed model improves on the samples drawn from the NMT with a higher BLEU score than the experimental model and the energy-based re-ranking algorithm. |
English-Indonesian Neural Machine Translation for Spoken Language Domains (P19-2)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) is a data-driven method that requires a large amount of data to build a robust model. |
| Approach: | They conduct a study on Neural Machine Translation (NMT) for English-Indonesian and Indonesian-English (ID-EN) they build NMT systems using the Transformer model for both translation directions and implement domain adaptation method to train pre-trained NMT on speech language data. |
| Outcome: | The proposed model can learn formal translation outputs for English-Indonesian and Indonesian-English (ID-EN) given a small dataset of speech-styled language and a larger dataset of less formal language, the proposed model will be useful for learning formality level. |