Graph-Based Translation Via Graph Segmentation

Abstract

In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese-English and German-English demonstrate that the context-aware segmen-tation significantly improves the baseline graph-based model.

Context-Aware Graph Segmentation for Graph-Based Translation

“…Following Li et al (2016), we define our model in the well-known log-linear framework (Och and Ney, 2002). In our experiments, we use the following standard features: two translation probabilities p(g, c|t) and p(t|g, c), two lexical translation probabilities p lex (g, c|t) and p lex (t|g, c), a language model p(t), a rule penalty, a word penalty, and a distortion function as defined in Galley and Manning (2010).…”

“…A system PBMT is built using the phrase-based model in Moses (Koehn et al, 2007). GBMT is the graph-based translation system described in Li et al (2016). To examine the influence of bigram links, GBMT is also used to translate dependency trees where treelets Xiong et al, 2007) are the basic translation units.…”

“…We further observed that, while our system achieves the best performance when all rules are used on ZH-EN, the combination of basic rules and segmenting rules on DE-EN results in the best system. This is probably because reordering (including long-distance reordering) is performed less often in DE-EN than in ZH-EN (Li et al, 2016) which makes selecting rules less preferable on DE-EN.…”

“…To make use of the merits of both phrase-based models and treelet-based models, Li et al (2016) proposed a graph-based translation model as in Equation (1):…”

“…However, continuous phrases which are not connected on trees and thus excluded could in fact be extremely important to system performance (Koehn et al, 2003;Hanneman and Lavie, 2009). To make use of the merits of both phrase-based models and treelet-based models, Li et al (2016) proposed a graph-based translation model as in Equation (1):…”