ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

• URL: http://arxiv.org/abs/2001.11121v1
• Date: Wed, 29 Jan 2020 22:44:05 GMT
• Title: ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
• Authors: Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang and Gerard de Melo
• Abstract summary: 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.
• Score: 48.287610663358066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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