Abstract
In this work, we present a POS-based preordering approach that tackles both long- and short-distance reordering phenomena. Syntactic unlexicalized reordering rules are automatically extracted from a parallel corpus using only word alignment and a source-side language tagging. The reordering rules are used in a deterministic manner; this prevents the decoding speed from being bottlenecked in the reordering procedure. A new approach for both rule filtering and rule application is used to ensure a fast and efficient reordering. The tests performed on the IWSLT2016 English-to-Arabic evaluation benchmark show a noticeable increase in the overall Blue Score for our system over the baseline PSMT system.
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Notes
- 1.
FindCS is a simple method that finds the number of crossing alignments (CS) for a given aligned sentence.
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- 3.
The conversion table can be found in the following link http://universaldependencies.org/tagset-conversion/en-penn-uposf.html.
- 4.
- 5.
We mean by a monotonic corpus, a corpus in which the alignment does not contain any crossing links.
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Hadj Ameur, M.S., Guessoum, A., Meziane, F. (2018). A POS-Based Preordering Approach for English-to-Arabic Statistical Machine Translation. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds) Arabic Language Processing: From Theory to Practice. ICALP 2017. Communications in Computer and Information Science, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-319-73500-9_3
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