Lattice Desegmentation for Statistical Machine Translation
Mohammad Yahya Bani Salameh, Colin Cherry, Grzegorz Kondrak · 2014
Morphological segmentation is an effec-tive sparsity reduction strategy for statis-tical machine translation (SMT) involv-ing morphologically complex languages. When translating into a segmented lan-guage, an extra step is required to deseg-ment the output; previous studies have de-segmented the 1-best output from the de-coder. In this paper, we expand our trans-lation options by desegmenting n-best lists or lattices. Our novel lattice desegmenta-tion 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 de-segmentation process, as well as an un-segmented language model (LM). We in-vestigate this technique in the context of English-to-Arabic and English-to-Finnish translation, showing significant improve-ments in translation quality over deseg-mentation of 1-best decoder outputs. 1