Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDUTNLP Machine Translation System for WMT21 Triangular Translation Task
نظام الترجمة الآلي Dutnlp لمهمة الترجمة الثلاثي WMT21
466
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تصف هذه الورقة تقديم LIT-NLP LAB إلى المهمة المشتركة للترجمة الثلاثي WMT-21 Triangular.لا يسمح للمشاركين باستخدام البيانات الأخرى واتجاه الترجمة لهذه المهمة هو الروسية إلى الصينية.في هذه المهمة، نستخدم المحول كنموذج الأساس لدينا، ودمج العديد من التقنيات لتعزيز أداء الأساس، بما في ذلك تصفية البيانات، واختيار البيانات، والضبط الناعم، والتحرير بعد التحرير.علاوة على ذلك، للاستفادة من موارد اللغة الإنجليزية، مثل البيانات الروسية / الإنجليزية والصينية / الإنجليزية الموازية، يتم إنشاء مثلث العلاقة من خلال أنظمة الترجمة الآلية العصبية متعددة اللغات.نتيجة لذلك، يحقق تقديمنا نقاطا بلو 21.9 في الروسية إلى الصينية.
This paper describes DUT-NLP Lab's submission to the WMT-21 triangular machine translation shared task. The participants are not allowed to use other data and the translation direction of this task is Russian-to-Chinese. In this task, we use the Transformer as our baseline model, and integrate several techniques to enhance the performance of the baseline, including data filtering, data selection, fine-tuning, and post-editing. Further, to make use of the English resources, such as Russian/English and Chinese/English parallel data, the relationship triangle is constructed by multilingual neural machine translation systems. As a result, our submission achieves a BLEU score of 21.9 in Russian-to-Chinese.
References used
https://aclanthology.org/
rate research
Read More
This paper describes Mininglamp neural machine translation systems of the WMT2021 news translation tasks. We have participated in eight directions translation tasks for news text including Chinese to/from English, Hausa to/from English, German to/fro
m English and French to/from German. Our fundamental system was based on Transformer architecture, with wider or smaller construction for different news translation tasks. We mainly utilized the method of back-translation, knowledge distillation and fine-tuning to boost single model, while the ensemble was used to combine single models. Our final submission has ranked first for the English to/from Hausa task.
This paper describes Tencent Translation systems for the WMT21 shared task. We participate in the news translation task on three language pairs: Chinese-English, English-Chinese and German-English. Our systems are built on various Transformer models
with novel techniques adapted from our recent research work. First, we combine different data augmentation methods including back-translation, forward-translation and right-to-left training to enlarge the training data. We also apply language coverage bias, data rejuvenation and uncertainty-based sampling approaches to select content-relevant and high-quality data from large parallel and monolingual corpora. Expect for in-domain fine-tuning, we also propose a fine-grained one model one domain'' approach to model characteristics of different news genres at fine-tuning and decoding stages. Besides, we use greed-based ensemble algorithm and transductive ensemble method to further boost our systems. Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering. Finally, our constrained Chinese-English system achieves 33.4 case-sensitive BLEU score, which is the highest among all submissions. The German-English system is ranked at second place accordingly.
This paper describes the ISTIC's submission to the Triangular Machine Translation Task of Russian-to-Chinese machine translation for WMT' 2021. In order to fully utilize the provided corpora and promote the translation performance from Russian to Chi
nese, the pivot method is used in our system which pipelines the Russian-to-English translator and the English-to-Chinese translator to form a Russian-to-Chinese translator. Our system is based on the Transformer architecture and several effective strategies are adopted to improve the quality of translation, including corpus filtering, data pre-processing, system combination and model ensemble.
In this paper, we describe our submissions for the Similar Language Translation Shared Task 2021. We built 3 systems in each direction for the Tamil ⇐⇒ Telugu language pair. This paper outlines experiments with various tokenization schemes to train statistical models. We also report the configuration of the submitted systems and results produced by them.
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures
, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
