A Relaxed Matching Procedure for Unsupervised BLI

Xu Zhao, Zihao Wang, Yong Zhang, Hao Wu

Abstract Paper Share

Machine Translation Short Paper

Session 6A: Jul 7 (05:00-06:00 GMT)
Session 8B: Jul 7 (13:00-14:00 GMT)
Abstract: Recently unsupervised Bilingual Lexicon Induction(BLI) without any parallel corpus has attracted much research interest. One of the crucial parts in methods for the BLI task is the matching procedure. Previous works impose a too strong constraint on the matching and lead to many counterintuitive translation pairings. Thus We propose a relaxed matching procedure to find a more precise matching between two languages. We also find that aligning source and target language embedding space bidirectionally will bring significant improvement. We follow the previous iterative framework to conduct experiments. Results on standard benchmark demonstrate the effectiveness of our proposed method, which substantially outperforms previous unsupervised methods.
You can open the pre-recorded video in a separate window.
NOTE: The SlidesLive video may display a random order of the authors. The correct author list is shown at the top of this webpage.

Similar Papers

Neural Graph Matching Networks for Chinese Short Text Matching
Lu Chen, Yanbin Zhao, Boer Lv, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu,
A representative figure from paper main.547
A Multi-Perspective Architecture for Semantic Code Search
Rajarshi Haldar, Lingfei Wu, JinJun Xiong, Julia Hockenmaier,
A representative figure from paper main.758
Neighborhood Matching Network for Entity Alignment
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao,
A representative figure from paper main.578
Harvesting and Refining Question-Answer Pairs for Unsupervised QA
Zhongli Li, Wenhui Wang, Li Dong, Furu Wei, Ke Xu,
A representative figure from paper main.600