Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMiSS@WMT21: Contrastive Learning-reinforced Domain Adaptation in Neural Machine Translation
ملكة جمال @ WMT21: تكيف مجال التعلم المعزز بالتعلم في الترجمة الآلية العصبية
524
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في هذه الورقة، نصف نظام ملكة جمالنا الذي شارك في مهمة ترجمة WMT21 الأخبار. شاركنا بشكل رئيسي في تقييم اتجاهات الترجمة الثلاثة لمهام الترجمة الإنجليزية واليابانية والإنجليزية. في النظم المقدمة، تعتبر في المقام الأول شبكات أوسع، وشبكات أعمق، والترميز الموضعي النسبي، والشبكات التنافعية الديناميكية من حيث هيكل النماذج، في حين أننا من حيث التدريب، حققنا في تكييف المجال المعزز للتناقض في التعلم، والتدريب والإشراف على الذات، والتحسين طرق التدريب التبديل الموضوعية. وفقا لنتائج التقييم النهائي، يمكن لشبكة أعمق وأوسع وأقوى تحسين أداء الترجمة بشكل عام، ومع ذلك يمكن أن تحسن طريقة توطين نطاق البيانات لدينا الأداء أكثر. بالإضافة إلى ذلك، وجدنا أن التبديل إلى استخدام هدفنا المقترح خلال المرحلة الفائقة باستخدام البيانات الصغيرة المرتبطة بالنطاق نسبيا يمكن أن يحسن بشكل فعال من استقرار تقارب النموذج وتحقيق الأداء الأمثل بشكل أفضل.
In this paper, we describe our MiSS system that participated in the WMT21 news translation task. We mainly participated in the evaluation of the three translation directions of English-Chinese and Japanese-English translation tasks. In the systems submitted, we primarily considered wider networks, deeper networks, relative positional encoding, and dynamic convolutional networks in terms of model structure, while in terms of training, we investigated contrastive learning-reinforced domain adaptation, self-supervised training, and optimization objective switching training methods. According to the final evaluation results, a deeper, wider, and stronger network can improve translation performance in general, yet our data domain adaption method can improve performance even more. In addition, we found that switching to the use of our proposed objective during the finetune phase using relatively small domain-related data can effectively improve the stability of the model's convergence and achieve better optimal performance.
References used
https://aclanthology.org/
rate research
Read More
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.
In this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain.Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited da
ta, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial Intelligence due to spe-cific terminology, etc. We propose DomainSpecific Back Translation method whichuses available monolingual data and gen-erates synthetic data in a different way.This approach uses Out Of Domain words.The approach is very generic and can beapplied to any language pair for any domain. We conduct our experiments onChemistry and Artificial Intelligence do-mains for Hindi and Telugu in both direc-tions. It has been observed that the usageof synthetic data created by the proposedalgorithm improves the BLEU scores significantly.
This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or target language in the new domain. We propose a cross-lingual data selec
tion method to extract in-domain sentences in the missing language side from a large generic monolingual corpus. Our proposed method trains an adaptive layer on top of multilingual BERT by contrastive learning to align the representation between the source and target language. This then enables the transferability of the domain classifier between the languages in a zero-shot manner. Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks. We evaluate our cross-lingual data selection method on NMT across five diverse domains in three language pairs, as well as a real-world scenario of translation for COVID-19. The results show that our proposed method outperforms other selection baselines up to +1.5 BLEU score.
We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues. As a first step, we propose to fragment data into phrase pairs and use a random sampl
e to fine-tune a generic NMT model instead of the full sentences. Despite the loss of long segments for the sake of confidentiality protection, we find that NMT quality can considerably benefit from this adaptation, and that further gains can be obtained with a simple tagging technique.
Production NMT systems typically need to serve niche domains that are not covered by adequately large and readily available parallel corpora. As a result, practitioners often fine-tune general purpose models to each of the domains their organisation
caters to. The number of domains however can often become large, which in combination with the number of languages that need serving can lead to an unscalable fleet of models to be developed and maintained. We propose Multi Dimensional Tagging, a method for fine-tuning a single NMT model on several domains simultaneously, thus drastically reducing development and maintenance costs. We run experiments where a single MDT model compares favourably to a set of SOTA specialist models, even when evaluated on the domain those baselines have been fine-tuned on. Besides BLEU, we report human evaluation results. MDT models are now live at Booking.com, powering an MT engine that serves millions of translations a day in over 40 different languages.
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
