---
title: "Modeling Lexical Cohesion for Document-Level Machine Translation"
authors:
  - "Deyi Xiong"
  - "Guosheng Ben"
  - "Min Zhang"
  - "Yajuan Lv"
  - "Qun Liu"
doi: ""
url: "https://ijcai.org/proceedings/2013/322"
pdf: "https://ijcai.org/proceedings/2013/0322.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2183-2188"
track: ""
keywords:
---

# Modeling Lexical Cohesion for Document-Level Machine Translation

Deyi Xiong, Guosheng Ben, Min Zhang, Yajuan Lv, Qun Liu

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2183-2188.

## Abstract

Lexical cohesion arises from a chain of lexical items that establish links between sentences in a text. In this paper we propose three different models to capture lexical cohesion for document-level machine translation: (a) a direct reward model where translation hypotheses are rewarded whenever lexical cohesion devices occur in them, (b) a conditional probability model where the appropriateness of using lexical cohesion devices is measured, and (c) a mutual information trigger model where a lexical cohesion relation is considered as a trigger pair and the strength of the association between the trigger and the triggered item is estimated by mutual information. We integrate the three models into hierarchical phrase-based machine translation and evaluate their effectiveness on the NIST Chinese-English translation tasks with large-scale training data. Experiment results show that all three models can achieve substantial improvements over the baseline and that the mutual information trigger model performs better than the others.