---
title: "Discriminative Reordering Model Adaptation via Structural Learning"
authors:
  - "Biao Zhang"
  - "Jinsong Su"
  - "Deyi Xiong"
  - "Hong Duan"
  - "Junfeng Yao"
doi: ""
url: "https://ijcai.org/proceedings/2015/151"
pdf: "https://ijcai.org/proceedings/2015/0151.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1040-1045"
track: "Main Track"
keywords:
---

# Discriminative Reordering Model Adaptation via Structural Learning

Biao Zhang, Jinsong Su, Deyi Xiong, Hong Duan, Junfeng Yao

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 1040-1045.

## Abstract

Reordering model adaptation remains a big challenge in statistical machine translation because reordering patterns of translation units often vary dramatically from one domain to another. In this paper, we propose a novel adaptive discriminative reordering model (DRM) based on structural learning, which can capture correspondences among reordering features from two different domains. Exploiting both in-domain and out-of-domain monolingual corpora, our model learns a shared feature representation for cross-domain phrase reordering. Incorporating features of this representation, the DRM trained on out-of-domain corpus generalizes better to in-domain data. Experiment results on the NIST Chinese-English translation task show that our approach significantly outperforms a variety of baselines.