Learning sign language machine translation based on elastic net regularization and latent semantic analysis

Abstract

In this paper, we present a new sign language machine translation approach based on regression method. The aim of this work is to improve the translation quality and accuracy of existing regularized regression methods. Our approach represents a methodological foundation for small-scale corpus domains such as the Sign Language Machine Translation field. Our method is based on the Elastic net regularization using linear combination of the L1 and L2 penalties of the lasso and ridge methods. We show that using both the de-bruijn graph with the Latent Semantic Analysis technique in the decoding process improves the translation results. The system is experimented on American Sign Language parallel corpora containing 300 sentences and assessed by BLEU, METEOR, NIST and F1-MESURE machine translation evaluation metrics. We obtained good experimental results compared to classical phrase based approach i.e MOSES framework. Also our approach improved the translation results compared to LASSO and RIDGE regression approaches.