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Productive Generation of Compound Words in Statistical Machine Translation

Sara Stymne, Nicola Cancedda
In many languages the use of compound words is very productive. A common practice to reduce sparsity consists in splitting compounds in the training data. When this is done, the system incurs the risk of translating components in non-consecutive positions, or in the wrong order. Furthermore, a post-processing step of compound merging is required to reconstruct compound words in the output. We present a method for increasing the chances that components that should be merged are translated into contiguous positions and in the right order. We also propose new heuristic methods for merging components that outperform all known methods, and a learning-based method that has similar accuracy as the heuristic method, is better at producing novel compounds, and can operate with no background In many languages the use of compound words is very productive. A common practice to reduce sparsity consists in splitting compounds in the training data. When this is done, the system incurs the risk of translating components in non-consecutive positions, or in the wrong order. Furthermore, a post-processing step of compound merging is required to reconstruct compound words in the output. We present a method for increasing the chances that components that should be merged are translated into contiguous positions and in the right order. We also propose new heuristic methods for merging components that outperform all known methods, and a learning-based method that has similar accuracy as the heuristic method, is better at producing novel compounds, and can operate with no background In many languages the use of compound words is very productive. A common practice to reduce sparsity consists in splitting compounds in the training data. When this is done, the system incurs the risk of translating components in non-consecutive positions, or in the wrong order. Furthermore, a post-processing step of compound merging is required to reconstruct compound words in the output. We present a method for increasing the chances that components that should be merged are translated into contiguous positions and in the right order. We also propose new heuristic methods for merging components that outperform all known methods, and a learning-based method that has similar accuracy as the heuristic method, is better at producing novel compounds, and can operate with no background linguistic resources.
EMNLP, 6th Workshop on Statistical Machine Translation, Edinburgh, UK, July 30-31.
2011
2011/015