The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translation. Neural interlingua representations have been shown as an effective method for achieving this. However, fixed-length neural interlingua representations introduced in previous work can limit its flexibility and representation ability. In this study, we introduce a novel method to enhance neural interlingua representations by making their length variable, thereby overcoming the constraint of fixed-length neural interlingua representations. Our empirical results on zero-shot translation on OPUS, IWSLT, and Europarl datasets demonstrate stable model convergence and superior zero-shot translation results compared to fixed-length neural interlingua representations. However, our analysis reveals the suboptimal efficacy of our approach in translating from certain source languages, wherein we pinpoint the defective model component in our proposed method.

翻译：多语言神经机器翻译（MNMT）模型中编码表示的语种独立性对其在零样本翻译上的泛化能力至关重要。神经中间语言表示已被证明是实现这一目标的有效方法。然而，先前研究中提出的固定长度神经中间语言表示限制了其灵活性与表示能力。在本研究中，我们提出了一种通过使神经中间语言表示长度可变来增强其能力的新方法，从而克服了固定长度神经中间语言表示的局限。我们在OPUS、IWSLT和Europarl数据集上的零样本翻译实验结果表明，与固定长度神经中间语言表示相比，该方法实现了稳定的模型收敛和更优的零样本翻译效果。然而，分析发现该方法在翻译某些源语言时效果欠佳，我们进一步定位了所提出方法中的缺陷模型组件。