Creating a strong statistical machine translation system by combining different decoders

Machine translation is a very important field in Natural Language Processing. The need for machine translation arises due to the increasing amount of data available online. Most of our data now is digital and this is expected to increase over time. Since human manual translation takes a lot of time and effort, machine translation is needed to cover all of the languages available. A lot of research has been done to make machine translation faster and more reliable between different language pairs. Machine translation is now being coupled with deep learning and neural networks. New topics in machine translation are being studied and tested like applying neural machine translation as a replacement to the classical statistical machine translation. In this thesis, we also study the effect of data-preprocessing and decoder type on translation output. We then demonstrate two ways to enhance translation from English to Arabic. The first approach uses a two-decoder system; the first decoder translates from English to Arabic and the second is a post-processing decoder that retranslates the first Arabic output to Arabic again to fix some of the translation errors. We then study the results of different kinds of decoders and their contributions to the test set. The results of this study lead to the second approach which combines different decoders to create a stronger one. The second approach uses a classifier to categorize the English sentences based on their structure. The output of the classifier is the decoder that is suited best to translate the English sentence. Both approaches increased the BLEU score albeit with different ranges. The classifier showed an increase of ~0.1 BLEU points while the post-processing decoder showed an increase of between ~0.3~11 BLEU points on two different test sets. Eventually we compare our results to Google translate to know how well we are doing in comparison to a well-known translator. Our best translation machine system scored 5 absolute points compared to Google translate in ISI corpus test set and we were 9 absolute points lower in the case of the UN corpus test set

Lattice desegmentation for statistical machine translation

Abstract Morphological segmentation is an effective sparsity reduction strategy for statistical machine translation (SMT) involving morphologically complex languages. When translating into a segmented language, an extra step is required to desegment the output; previous studies have desegmented the 1-best output from the decoder. In this paper, we expand our translation options by desegmenting n-best lists or lattices. Our novel lattice desegmentation algorithm effectively combines both segmented and desegmented views of the target language for a large subspace of possible translation outputs, which allows for inclusion of features related to the desegmentation process, as well as an unsegmented language model (LM). We investigate this technique in the context of English-to-Arabic and English-to-Finnish translation, showing significant improvements in translation quality over desegmentation of 1-best decoder outputs