Abstract
We discuss the importance of domain specific language model in statistical machine translation system. Both the structures and phrase selection are not the same for different domains. So, the language model trained with the general domain data or other domain data can not provide better accuracy. Moreover, there may have some specific focus in different texts of the same domain. Hence, the language model trained with data from the default domain may not yield significant output. In this paper, we learn our system dynamically based on the better matches with the input text. Instead of directly selecting pre-trained language model we prepare the prioritized language model according to the situation. The proposed model is evaluated for Hindi-English translation. It shows a significant improvement on the translated output in terms of the BLEU score. Our evaluation shows that automated domain adoption to predict better language model improves the translation quality.
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Banik, D., Ekbal, A., Bhattacharyya, P. (2023). Two-Phased Dynamic Language Model: Improved LM for Automated Language Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_19
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