Linguistically Inspired Language Model Augmentation for MT

Abstract

The present article reports on efforts to improve the translation accuracy of a corpus―based Machine Translation (MT) system. In order to achieve that, an error analysis performed on past translation outputs has indicated the likelihood of improving the translation accuracy by augmenting the coverage of the Target-Language (TL) side language model. The method adopted for improving the language model is initially presented, based on the concatenation of consecutive phrases. The algorithmic steps are then described that form the process for augmenting the language model. The key idea is to only augment the language model to cover the most frequent cases of phrase sequences, as counted over a TL-side corpus, in order to maximize the cases covered by the new language model entries. Experiments presented in the article show that substantial improvements in translation accuracy are achieved via the proposed method, when integrating the grown language model to the corpus-based MT system.

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