Example-based bi-directional Chinese-English machine translation with semi-automatically induced grammars

We have previously developed a framework for bi-directional English-to-Chinese/Chineseto-English machine translation using semi-automatically induced grammars from unannotated corpora. The framework adopts an example-based machine translation (EBMT) approach. This work reports on three extensions to the framework. First, we investigate the comparative merits of three distance metrics (Kullback-Leibler, ManhattanNorm and Gini Index) for agglomerative clustering in grammar induction. Second, we seek an automatic evaluation method that can also consider multiple translation outputs generated for a single input sentence based on the BLEU metric. Third, our previous investigation shows that Chinese-to-English translation has lower performance due to incorrect use of English inflectional forms a consequence of random selection among translation alternatives. We present an improved selection strategy that leverages information from the example parse trees in our EBMT paradigm.