Abstract
This work demonstrates a writing assistant system that provides high level advice for Chinese scientific writing.
Cross-lingual approaches are investigated to analyze the information structure of a given Chinese abstract and retrieve useful knowledge in the related work written in both English and Chinese.
To the best of our knowledge, this is the first study on Chinese information structure identification.
Without the need of labeled Chinese data, our novel model is capable of dealing with Chinese instances by acquiring language-invariant knowledge from the labeled English data. Adversarial learning is employed to enhance the cross-lingual sentence representation.
Keywords
- Multidisciplinary Topics and Applications: Computer-Aided Education
- Natural Language Processing: Natural Language Processing
- Natural Language Processing: NLP Applications and Tools
