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Register a new userZero-Resource Translation with Multi-Lingual Neural Machine Translation
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Added by
Orhan Firat
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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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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Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual Encoder-Classifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks - Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks.
Previous works mainly focus on improving cross-lingual transfer for NLU tasks with multilingual pretrained encoder (MPE), or improving the translation performance on NMT task with BERT. However, how to improve the cross-lingual transfer of NMT model with multilingual pretrained encoder is under-explored. In this paper, we focus on a zero-shot cross-lingual transfer task in NMT. In this task, the NMT model is trained with one parallel dataset and an off-the-shelf MPE, then is directly tested on zero-shot language pairs. We propose SixT, a simple yet effective model for this task. The SixT model leverages the MPE with a two-stage training schedule and gets further improvement with a position disentangled encoder and a capacity-enhanced decoder. The extensive experiments prove that SixT significantly improves the translation quality of the unseen languages. With much less computation cost and training data, our model achieves better performance on many-to-English testsets than CRISS and m2m-100, two strong multilingual NMT baselines.
Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side. To address this challenge, we propose an effective transfer learning approach based on cross-lingual pre-training. Our key idea is to make all source languages share the same feature space and thus enable a smooth transition for zero-shot translation. To this end, we introduce one monolingual pre-training method and two bilingual pre-training methods to obtain a universal encoder for different languages. Once the universal encoder is constructed, the parent model built on such encoder is trained with large-scale annotated data and then directly applied in zero-shot translation scenario. Experiments on two public datasets show that our approach significantly outperforms strong pivot-based baseline and various multilingual NMT approaches.
We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT14 benchmarks, a single multilingual model achieves comparable performance for English$rightarrow$French and surpasses state-of-the-art results for English$rightarrow$German. Similarly, a single multilingual model surpasses state-of-the-art results for French$rightarrow$English and German$rightarrow$English on WMT14 and WMT15 benchmarks respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.
Prior work has proved that Translation memory (TM) can boost the performance of Neural Machine Translation (NMT). In contrast to existing work that uses bilingual corpus as TM and employs source-side similarity search for memory retrieval, we propose a new framework that uses monolingual memory and performs learnable memory retrieval in a cross-lingual manner. Our framework has unique advantages. First, the cross-lingual memory retriever allows abundant monolingual data to be TM. Second, the memory retriever and NMT model can be jointly optimized for the ultimate translation goal. Experiments show that the proposed method obtains substantial improvements. Remarkably, it even outperforms strong TM-augmented NMT baselines using bilingual TM. Owning to the ability to leverage monolingual data, our model also demonstrates effectiveness in low-resource and domain adaptation scenarios.
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