Language technologies are far from replacing human language skills, as machines struggle with linguistic diversity and cultural nuances.
In an increasingly interconnected world, language barriers have become a significant challenge. However, with the advent of automated translation tools, some argue that humans no longer need to learn other languages. This belief is gaining traction, leading to the dismantling of language programs in universities. But are machines truly capable of replacing human language skills? In this article, we delve into the limitations of machine translation and explore why humans still need to cultivate multilingual talent.
How Machines Learn Languages:
Machine translation relies on algorithms trained on vast amounts of text data to identify patterns and probabilities of word usage. Bilingual training data, typically based on standard language versions, is used to train these algorithms. However, the diversity inherent in human languages poses a challenge for machines. Variations in dialects, slang, and cultural nuances often go unrecognized, leading to inaccurate translations. For instance, the Arabic phrase “يصبحهم” meaning “good morning” was mistranslated as “attack them” by an automated translation tool.
The Challenge of Linguistic Diversity:
Languages exhibit internal diversity, with variations in tense, number, gender, and grammatical encoding. While some languages require specific grammatical markers, others do not. Machines struggle to account for these differences, leading to errors in translation. For example, translating the simple English statement “I am a student” into German necessitates the inclusion of a grammatical gender marking, resulting in a translation that specifies the student’s gender.
The Dominance of English:
English enjoys a dominant position in machine learning, with over 90% of training data for large language models being in English. This disparity erases the linguistic diversity of other languages and perpetuates the dominance of English. Machine translation into English often appears accurate due to the availability of bilingual and monolingual training data. However, translations into languages other than English are riddled with mistakes and inconsistencies.
The Limitations of Machine Translation:
While machine translation can provide a general understanding of websites or assist with basic communication, its limitations become evident in high-stakes contexts. In healthcare settings, for example, relying solely on translation apps can lead to critical errors in patient instructions. Machine-translated information on COVID-19 testing into German included invented words, grammatical errors, and inconsistencies. Translation apps should only be used in low-risk situations, with human interpreters being the preferred choice for accurate communication.
The Importance of Human Multilingual Talent:
Only humans can assess the risk level of a situation and determine whether machine translation is appropriate. To make informed decisions, individuals need a deep understanding of both languages and machine learning. Increasing reliance on machines for language learning undermines the development of advanced language proficiency in humans. In fields such as economics, diplomacy, and healthcare, the ability to communicate across language barriers is essential. By outsourcing language tasks to machines, we risk dehumanizing the act of communication and diminishing the importance of language in building relationships and communities.
Conclusion:
While machine translation has made significant advancements, it is far from replacing human language skills. Machines struggle with linguistic diversity, cultural nuances, and the complexities of human communication. Translation apps should be used cautiously in low-risk situations, with human interpreters remaining the preferred choice for accurate and meaningful communication. Rather than dismantling language programs, we should recognize the value of human multilingual talent in fostering understanding, building relationships, and navigating the complexities of a diverse world.
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