Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot
  Translation
Changtong Zan Liang Ding Li Shen Yibin Lei Yibing Zhan Weifeng Liu Dacheng Tao
Zero-shot translation (ZST), which is generally based on a multilingual
neural machine translation model, aims to translate between unseen language
pairs in training data. The common practice to guide the zero-shot language
mapping during inference is to deliberately insert the source and target
language IDs, e.g., <EN> for English and <DE> for German. Recent studies have
shown that language IDs sometimes fail to navigate the ZST task, making them
suffer from the off-target problem (non-target language words exist in the
generated translation) and, therefore, difficult to apply the current
multilingual translation model to a broad range of zero-shot language
scenarios. To understand when and why the navigation capabilities of language
IDs are weakened, we compare two extreme decoder input cases in the ZST
directions: Off-Target (OFF) and On-Target (ON) cases. By contrastively
visualizing the contextual word representations (CWRs) of these cases with
teacher forcing, we show that 1) the CWRs of different languages are
effectively distributed in separate regions when the sentence and ID are
matched (ON setting), and 2) if the sentence and ID are unmatched (OFF
setting), the CWRs of different languages are chaotically distributed. Our
analyses suggest that although they work well in ideal ON settings, language
IDs become fragile and lose their navigation ability when faced with off-target
tokens, which commonly exist during inference but are rare in training
scenarios. In response, we employ unlikelihood tuning on the negative (OFF)
samples to minimize their probability such that the language IDs can
discriminate between the on- and off-target tokens during training. Experiments
spanning 40 ZST directions show that our method reduces the off-target ratio by
-48.0% on average, leading to a +9.1 BLEU improvement with only an extra +0.3%
tuning cost.