Assessing AI Translation of Humor: challenges, limitations and post editing solutions
DOI:
https://doi.org/10.25130/jfa.conf.10.5.15Keywords:
humor, post-edit, translation, Translation Edit RateAbstract
Humor is widely regarded as a compelling aspect of everyday communication, offering nuanced, often playful insights into political, social, and cultural realities. Despite its significance, humor remains a challenging and under-theorized area in the field of machine translation. This paper principally opts for addressing this somewhat under-theorized topic through (1) presenting a thorough account of humor in terms of the definitions, major types, motivations, and communicative functions; and (2) pinpointing and defining the way exploited by AI-generated translation programs in translating humor to show whether the renderings arrived at are appropriate, effective, and to the point or otherwise. It is hypothesized that translating humor poses untold and still unsolved problems for AI-generated translation programs, as they do not possess the proper systematized and refined algorithms that enable them to efficiently process the SL message and adequately transfer their pragmatic content into the TL. In order to test the validity of this hypothesis, a body of authentic humorous texts is chosen, a translation model is adopted to subtly post-edit renderings as presented by the AI-chosen programs, data analysis is delicately conducted, and some reference renderings are recommended. Translation quality is assessed using the Translation Edit Rate (TER) metric, which measures the effort required to correct them.
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