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Register a new userMultilingual Machine Translation Systems at WAT 2021: One-to-Many and Many-to-One Transformer based NMT
أنظمة الترجمة الآلية متعددة اللغات في Wat 2021: محول واحد إلى كثير ومحول محول
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في هذه الورقة، نقدم تفاصيل النظم التي قدمناها مقابل WAT 2021 Multiindicmt: مهمة متعددة اللغات.لقد قدمنا نماذج NMT متعددة اللغات منفصلة: واحد للغة الإنجليزية إلى 10 لغات ind وآخر ل 10 لغات ind للغة الإنجليزية.نناقش تفاصيل تنفيذ نهجين منفصلين متعدد اللغات NMT، وهما واحدا وكثير من الأحيان والعديد من إلى واحد، والذي يستفيد من وحدة فك ترميز مشتركة ومشمير مشترك، على التوالي.من تجاربنا، نلاحظ أن أنظمة NMT متعددة اللغات تتفوق على أنظمة طيران الأساس ثنائية اللغة لكل من أزواج اللغة قيد الدراسة.
In this paper, we present the details of the systems that we have submitted for the WAT 2021 MultiIndicMT: An Indic Language Multilingual Task. We have submitted two separate multilingual NMT models: one for English to 10 Indic languages and another for 10 Indic languages to English. We discuss the implementation details of two separate multilingual NMT approaches, namely one-to-many and many-to-one, that makes use of a shared decoder and a shared encoder, respectively. From our experiments, we observe that the multilingual NMT systems outperforms the bilingual baseline MT systems for each of the language pairs under consideration.
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This paper describes the work and the systems submitted by the IIIT-Hyderbad team in the WAT 2021 MultiIndicMT shared task. The task covers 10 major languages of the Indian subcontinent. For the scope of this task, we have built multilingual systems
for 20 translation directions namely English-Indic (one-to- many) and Indic-English (many-to-one). Individually, Indian languages are resource poor which hampers translation quality but by leveraging multilingualism and abundant monolingual corpora, the translation quality can be substantially boosted. But the multilingual systems are highly complex in terms of time as well as computational resources. Therefore, we are training our systems by efficiently se- lecting data that will actually contribute to most of the learning process. Furthermore, we are also exploiting the language related- ness found in between Indian languages. All the comparisons were made using BLEU score and we found that our final multilingual sys- tem significantly outperforms the baselines by an average of 11.3 and 19.6 BLEU points for English-Indic (en-xx) and Indic-English (xx- en) directions, respectively.
The choice of parameter sharing strategy in multilingual machine translation models determines how optimally parameter space is used and hence, directly influences ultimate translation quality. Inspired by linguistic trees that show the degree of rel
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Developing a unified multilingual model has been a long pursuing goal for machine translation. However, existing approaches suffer from performance degradation - a single multilingual model is inferior to separately trained bilingual ones on rich-res
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Multilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6).
Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on seven multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.
In this paper we describe our submissions to WAT-2021 (Nakazawa et al., 2021) for English-to-Myanmar language (Burmese) task. Our team, ID: YCC-MT1'', focused on bringing transliteration knowledge to the decoder without changing the model. We manuall
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