利用语义与语言特征改进多语言神经机器翻译
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
AI总结：
本文提出在编码器端解耦语义与语言特征以促进跨语言知识迁移，在解码器端引入语言编码器辅助目标语言生成，从而显著提升多语言神经机器翻译的零样本性能。
摘要：
多对多多语言神经机器翻译可被视为整合源语句的语义特征与目标语句的语言特征的过程。为增强零样本翻译，模型需要跨语言共享知识，这可以通过学习通用表示或跨语言映射的辅助任务来实现。为此，我们提出利用多语言之间的语义和语言特征来增强多语言翻译。在编码器端，我们引入一个解耦学习任务，通过解耦语义和语言特征来对齐编码器表示，从而在保留完整信息的同时促进知识迁移。在解码器端，我们利用一个语言编码器来整合低层语言特征，以辅助目标语言的生成。在多语言数据集上的实验结果表明，与基线系统相比，零样本翻译性能显著提升，同时保持了有监督翻译的性能。进一步的分析验证了我们的方法在利用语义和语言特征方面的有效性。代码可在 https://github.com/ictnlp/SemLing-MNMT 获取。
英文摘要：
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the target sentences. To enhance zero-shot translation, models need to share knowledge across languages, which can be achieved through auxiliary tasks for learning a universal representation or cross-lingual mapping. To this end, we propose to exploit both semantic and linguistic features between multiple languages to enhance multilingual translation. On the encoder side, we introduce a disentangling learning task that aligns encoder representations by disentangling semantic and linguistic features, thus facilitating knowledge transfer while preserving complete information. On the decoder side, we leverage a linguistic encoder to integrate low-level linguistic features to assist in the target language generation. Experimental results on multilingual datasets demonstrate significant improvement in zero-shot translation compared to the baseline system, while maintaining performance in supervised translation. Further analysis validates the effectiveness of our method in leveraging both semantic and linguistic features. The code is available at https://github.com/ictnlp/SemLing-MNMT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
