# Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning

arXiv: https://arxiv.org/abs/1405.5654 · HTML: https://mlsanity.com/paper/1405.5654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2014.

## Built on by (most cited first, 1 of 1)

- [NLP for Ghanaian Languages](https://mlsanity.com/paper/2103.15475/) (2021)

## Abstract (arXiv)

Although the parallel corpus has an irreplaceable role in machine translation, its scale and coverage is still beyond the actual needs. Non-parallel corpus resources on the web have an inestimable potential value in machine translation and other natural language processing tasks. This article proposes a semi-supervised transductive learning method for expanding the training corpus in statistical machine translation system by extracting parallel sentences from the non-parallel corpus. This method only requires a small amount of labeled corpus and a large unlabeled corpus to build a high-performance classifier, especially for when there is short of labeled corpus. The experimental results show that by combining the non-parallel corpus alignment and the semi-supervised transductive learning method, we can more effectively use their respective strengths to improve the performance of machine translation system.