Improving Machine Translation Performance through Idiom Paraphrasing

Metaphors are powerful tools for making sense of complex emotions and experiences, particularly in mental health. Researchers have identified two key challenges in translating metaphors for mental health care: metaphor identification and understanding. Automatic detection of mental illness from social media posts using metaphorical mappings has proven to be highly effective, helping users understand their mental health status early on. However, machine translation faces significant challenges in dealing with figurative language, including metaphor.
Theoretical and historical insights suggest that machines struggle with translating natural language, particularly metaphor, due to the complexity of linguistic problems. While computer systems have made significant progress in understanding language, they will never reach the depth of human knowledge. Recent advances in interlingual systems, translation models based on abstract language-neutral representations, offer hope for improving machine translation in this area.
To demystify complex concepts, let’s use everyday language and engaging metaphors or analogies. For instance, understanding how machines process language is like trying to understand a complex network of interconnected machines – it can be challenging to pinpoint the source of a problem, but with time and effort, we can improve our ability to diagnose and treat it.
In summary, machine translation for metaphor in mental health care faces significant challenges, but ongoing research offers promising solutions. By demystifying complex concepts and using everyday language, we can better understand the potential of machine translation to improve mental health care.