Improving Zero-shot Translation with Language-Independent Constraints

By Ngoc-Quan Pham, Jan Niehues, Thanh-Le Ha, A. Waibel. 2019

65 citations recorded by the source corpus.

An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages. In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, background): “In NLP, zero-shot learning work also includes translating between unseen language pairs (Johnson et al., 2017; Pham et al., 2019), language modeling on unseen languages (Lauscher et al.”
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing (2020, background): “pecially between related languages (Johnson et al., 2017; Gu et al., 2018; Pham et al., 2019).”
A Survey of Multilingual Neural Machine Translation (2019, methodology): “The simple zero-shot system described above, though promising, belies these expectations and its performance is generally lower than the pivot translation system [4, 56, 70, 113].”
Neural Machine Translation for Low-resource Languages: A Survey (2021, background): “Two solutions are available to solve this problem: to make the source explicitly target language independent [6, 69, 147, 166], and to improve model capacity [54, 206].”
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021, background): “, 2019), few/zero-shot learning (Johnson et al., 2017; Blissett and Ji, 2019; Pham et al., 2019; Abad et al., 2020) which uses only a few examples from the low-resource domain to adapt models trained in another domain, and polyglot learning (Cotterell and Heigold, 2017; Tsvetkov et al.”