论文标题
缺席：双向gan的跨语性句子代表映射
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
论文作者
论文摘要
对于我们可以使用大量平行文本的情况，已经提出了许多基于神经网络的跨语性转移学习方法。但是，在许多现实世界中，并行注释培训数据的大小受到限制。此外，先前的跨语性映射研究主要集中在单词级别上。这就提出了一个问题，即是否也可以将这种技术应用于毫不费力地获得交叉一致的句子表示。为此，我们提出了一个嵌入映射（缺少）框架的对抗性双向句子，该句子从有限数量的并行数据中学习了跨语性句子表示的映射。
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data.
