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Register a new userApproaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task
اقترب ترجمة لغة الإشارة لغة اللغات باعتبارها مهمة ترجمة ذات صالة منخفضة الموارد
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تعلق نظام ترجمة لغة الإشارة المتتالية في خرائط أول خريطة توقيع مقاطع فيديو لمعالجة التوضيحية ثم تترجم لمعان اللغات في لغات منطوقة.يركز هذا العمل على مكون الترجمة اللامع في المرحلة الثانية، وهو أمر صعب بسبب ندرة البيانات الموازية المتاحة للجمهور.نحن نقترب الترجمة اللمعان كمهامة ترجمة آلية منخفضة الموارد والتحقيق في طريقتين شعبيتين لتحسين جودة الترجمة: فرط HyperParameter و Backtranslation.نناقش الإمكانات والمخاطر من هذه الأساليب بناء على تجارب في مجموعة بيانات RWTH-Phoenix-Weather 2014T.
A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.
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