Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTermMind: Alibaba's WMT21 Machine Translation Using Terminologies Task Submission
TermMind: الترجمة الآلية ل ALIBABA من WMT21 باستخدام مصطلحات تقديم المهمة
373
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تصف هذه الورقة عملنا في الترجمة الآلية لعام 2021 باستخدام المهمة المشتركة للمصطلحات.نشارك في مهمة مصطلحات الترجمة المشتركة في اللغة الإنجليزية إلى زوج اللغة الصينية.لإرضاء قيود المصطلحات على الترجمة، نستخدم استراتيجية تكبير بيانات المصطلحات بناء على نموذج المحول.استخدمنا علامات لوضع علامة وإضافة ترجمة المصطلح إلى الجمل المتطابقة.أنشأنا مصطلحات صناعية باستخدام عبارات تم استخراجها من Corpus ثنائية اللغة لزيادة نسبة ترجمات المصطلح في بيانات التدريب.يتم استخدام المعالجة المفصلة المسبقة والتصفية على البيانات، يتم استخدام أسلوب Finetuning في المجال والكفر في نظامنا.تقدمنا يحصل على نتائج تنافسية في التقييم المستهدف للمصطلحات.
This paper describes our work in the WMT 2021 Machine Translation using Terminologies Shared Task. We participate in the shared translation terminologies task in English to Chinese language pair. To satisfy terminology constraints on translation, we use a terminology data augmentation strategy based on Transformer model. We used tags to mark and add the term translations into the matched sentences. We created synthetic terms using phrase tables extracted from bilingual corpus to increase the proportion of term translations in training data. Detailed pre-processing and filtering on data, in-domain finetuning and ensemble method are used in our system. Our submission obtains competitive results in the terminology-targeted evaluation.
References used
https://aclanthology.org/
rate research
Read More
This paper describes Kakao Enterprise's submission to the WMT21 shared Machine Translation using Terminologies task. We integrate terminology constraints by pre-training with target lemma annotations and fine-tuning with exact target annotations util
izing the given terminology dataset. This approach yields a model that achieves outstanding results in terms of both translation quality and term consistency, ranking first based on COMET in the En→Fr language direction. Furthermore, we explore various methods such as back-translation, explicitly training terminologies as additional parallel data, and in-domain data selection.
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary system
s are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.
Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusi
ng on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.
This paper describes the Global Tone Communication Co., Ltd.'s submission of the WMT21 shared news translation task. We participate in six directions: English to/from Hausa, Hindi to/from Bengali and Zulu to/from Xhosa. Our submitted systems are unco
nstrained and focus on multilingual translation odel, backtranslation and forward-translation. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.
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.
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
