Zero-Resource Translation with Multi-Lingual Neural Machine Translation
arXiv:1606.04164
Abstract
In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.
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Cited by in corpus (16)
- Unsupervised Machine Translation Using Monolingual Corpora Only
- Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
- A Teacher-Student Framework for Zero-Resource Neural Machine Translation
- Search Engine Guided Non-Parametric Neural Machine Translation
- Building a Neural Machine Translation System Using Only Synthetic Parallel Data
- Unsupervised Pretraining for Sequence to Sequence Learning
- Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning
- Multilingual Question Answering from Formatted Text applied to Conversational Agents
- Contextual Parameter Generation for Universal Neural Machine Translation
- Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
- A Study of Multilingual Neural Machine Translation
- Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs
- Pre-trained Language Model for Web-scale Retrieval in Baidu Search
- Multilingual Neural Machine Translation for Zero-Resource Languages
- A Survey on Low-Resource Neural Machine Translation
- Language-Independent Representor for Neural Machine Translation