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Master Thesis: Neural Sign Language Translation by Learning Tokenization by Orbay, Alptekin is a scholarly article available to read on EtoBox.

What is Master Thesis: Neural Sign Language Translation by Learning Tokenization about?

In this thesis, we propose a multitask learning based method to improve Neural Sign Language Translation (NSLT) consisting of two parts, a tokenization layer and Neural Machine Translation (NMT). The tokenization part focuses on how Sign Language (SL) videos should be represented to be fed into the other part. It has not been studied elaborately whereas NMT research has attracted several researchers contributing enormous advancements. Up to now, there are two main input tokenization levels, namely frame-level and gloss-level tokenization. Glosses are world-like intermediate presentation and unique to SLs. Therefore, we aim to develop a generic sign-level tokenization layer so that it is applicable to other domains without further effort. We begin with investigating current tokenization approaches and explain their weaknesses with several experiments. To provide a solution, we adapt Transfer Learning, Multitask Learning and Unsupervised Domain Adaptation into this research to leverage additional supervision. We succeed in enabling knowledge transfer between SLs and improve translation quality by 5 points in BLEU-4 and 8 points in ROUGE scores. Secondly, we show the effects of body p

Author

Orbay, Alptekin

Published

2020

Language

EN