Modeling Intensification for Sign Language Generation: A Computational Approach

• URL: http://arxiv.org/abs/2203.09679v1
• Date: Fri, 18 Mar 2022 01:13:21 GMT
• Title: Modeling Intensification for Sign Language Generation: A Computational Approach
• Authors: Mert \.Inan, Yang Zhong, Sabit Hassan, Lorna Quandt, Malihe Alikhani
• Abstract summary: End-to-end sign language generation models do not accurately represent the prosody in sign language. We aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner. We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics.
• Score: 13.57903290481737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner. We present different strategies grounded in linguistics of sign language that inform how intensity modifiers can be represented in gloss annotations. To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification. We then use a supervised intensity tagger to extend the annotated dataset and obtain labels for the remaining portion of it. This enhanced dataset is then used to train state-of-the-art transformer models for sign language generation. We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics. Human evaluation also indicates a higher preference of the videos generated using our model.

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