emnlp emnlp2013 emnlp2013-101 emnlp2013-101-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tian Xia ; Zongcheng Ji ; Shaodan Zhai ; Yidong Chen ; Qun Liu ; Shaojun Wang
Abstract: This paper proposes a multi-objective optimization framework which supports heterogeneous information sources to improve alignment in machine translation system combination techniques. In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to compact an exponential number of an potential hypotheses, and because better hypothesis alignment may benefit constructing better quality confusion networks, it is natural to add more useful information to improve alignment results. However, these information may be heterogeneous, so the widely-used Viterbi algorithm for searching the best alignment may not apply here. In the multi-objective optimization framework, each information source is viewed as an independent objective, and a new goal of improving all objectives can be searched by mature algorithms. The solutions from this framework, termed Pareto optimal solutions, are then combined to construct confusion networks. Experiments on two Chinese-to-English translation datasets show significant improvements, 0.97 and 1.06 BLEU points over a strong Indirected Hidden Markov Model-based (IHMM) system, and 4.75 and 3.53 points over the best single machine translation systems.