Interviews with eight signers show that emotional meaning in sign language comes from manual and non-manual cues built into the grammar, so translation tools should model faces, bodies, and signing dynamics, not just signs.
Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
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abstract
In this paper, we focus on how current Machine Translation (MT) tools perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper. We propose this evaluation framework based on the Multidimensional Quality Metrics (MQM) and perform a detailed error analysis of the MT outputs. From our analysis, we observe that about 50% of the MT outputs fail to preserve the original emotion. After further analysis of the errors, we find that emotion carrying words and linguistic phenomena such as polysemous words, negation, abbreviation etc., are common causes for these translation errors.
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Perspectives on Capturing Emotional Expressiveness in Sign Language
Interviews with eight signers show that emotional meaning in sign language comes from manual and non-manual cues built into the grammar, so translation tools should model faces, bodies, and signing dynamics, not just signs.