---
title: "Local Translation Prediction with Global Sentence Representation"
authors:
  - "Jiajun Zhang"
  - "Dakun Zhang"
  - "Jie Hao"
doi: ""
url: "https://ijcai.org/proceedings/2015/201"
pdf: "https://ijcai.org/proceedings/2015/0201.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1398-1403"
track: "Main Track"
keywords:
---

# Local Translation Prediction with Global Sentence Representation

Jiajun Zhang, Dakun Zhang, Jie Hao

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 1398-1403.

## Abstract

Statistical machine translation models have made great progress in improving the translation quality. However, the existing models predict the target translation with only the source- and target-side local context information. In practice, distinguishing good translations from bad ones does not only depend on the local features, but also rely on the global sentence-level information. In this paper, we explore the source-side global sentence-level features for target-side local translation prediction. We propose a novel bilingually-constrained chunk-based convolutional neural network to learn sentence semantic representations. With the sentence-level feature representation, we further design a feed-forward neural network to better predict translations using both local and global information. The large-scale experiments show that our method can obtain substantial improvements in translation quality over the strong baseline: the hierarchical phrase-based translation model augmented with the neural network joint model.