Question: What features can the multilingual model capture?
Evidence: Neural machine translation (NMT) exploits neural networks to directly learn to transform sentences from a source language to a target language BIBREF0 , BIBREF1 . Universal multilingual NMT discovered that a neural translation system can be trained on datasets containing source and target sentences in multiple languages BIBREF2 , BIBREF3 . Successfully trained models using this approach can be used to translate arbitrarily between any languages included in the training data. In low-resource scenarios, multilingual NMT has proven to be an extremely useful regularization method since each language direction benefits from the information of the others BIBREF4 , BIBREF5 .
An important research focus of multilingual NMT is zero-shot translation (ZS), or translation between languages included in multilingual data for which no directly parallel training data exists. Application-wise, ZS offers a faster and more direct path between languages compared to pivot translation, which requires translation to one or many intermediate languages. This can result in large latency and error propagation, common issues in non-end-to-end pipelines.From a representation learning point of view, there is evidence of NMT's ability to capture language-independent features, which have proved useful for cross-lingual transfer learning BIBREF6 , BIBREF7 and provide motivation for ZS translation. However it is still unclear if minimizing the difference in representations between languages is beneficial for zero-shot learning.
On the other hand, the current neural architecture and learning mechanisms of multilingual NMT is not geared towards having a common representation. Different languages are likely to convey the same semantic content with sentences of different lengths BIBREF8 , which makes the desiderata difficult to achieve. Moreover, the loss function of the neural translation model does not favour having sentences encoded in the same representation space regardless of the source language. As a result, if the network capacity is large enough, it may partition itself into different sub-spaces for different language pairs BIBREF9 .
Our work here focuses on the zero-shot translation aspect of universal multilingual NMT. First, we attempt to investigate the relationship of encoder representation and ZS performance. By modifying the Transformer architecture of BIBREF10 to afford a fixed-size representation for the encoder output, we found that we can significantly improve zero-shot performance at the cost of a lower performance on the supervised language pairs. To the best of our knowledge, this is the first empirical evidence showing that the multilingual model can capture both language-independent and language-dependent features, and that the former can be prioritized during training.
This observation leads us to the most important contribution in this work, which is to propose several techniques to learn a joint semantic space for different languages in multilingual models without any architectural modification. The key idea is to prefer a source language-independent representation in the decoder using an additional loss function. As a result, the NMT architecture remains untouched and the technique is scalable to the number of languages in the training data. The success of this method is shown by significant gains on zero-shot translation quality in the standard IWSLT 2017 multilingual benchmark BIBREF11 . Finally, we introduce a more challenging scenario that involves more than one bridge language between source and target languages. This challenging setup confirms the consistency of our zero-shot techniques while clarifying the disadvantages of pivot-based translation.
To the best of our knowledge, this is the first empirical evidence showing that the multilingual model can capture both language-independent and language-dependent features, and that the former can be prioritized during training.
Answer: Both language-independent and language-dependent features.