Read the following passage about Google and mark the letter A, B, C, or D on your answer sheet to indicate the best answer to each of the fo...

Read the following passage about Google and mark the letter A, B, C, or D on your answer sheet to indicate the best answer to each of the following questions from 31 to 40.

Google Translate has recently undergone a significant transformation by integrating a neural network into its translation framework, allowing it to process entire sentences rather than fragmented words or isolated phrases. This advanced system facilitates a more context-aware approach, enabling a higher degree of linguistic accuracy across multiple language pairs. A remarkable breakthrough of this neural translation model is its capacity to translate between two languages it has not been explicitly trained on, leveraging previously acquired linguistic structures. For instance, if the system has learned to translate between English and Japanese, as well as English and Korean, it can extrapolate this knowledge to translate directly between Japanese and Korean without resorting to English as an intermediary language.

This phenomenon, known as zero-shot translation, suggests that the system has devised an internal method of representing sentences with equivalent meanings in a shared linguistic space, bypassing conventional intermediary translation. Researchers at Google postulate that the system has developed an “interlingua,” a latent, abstract language that allows it to identify deep structural commonalities across otherwise unrelated languages. Though this artificial intermediary is neither readable nor comprehensible to humans, its existence underscores the system's ability to discern linguistic parallels on a fundamental level. Despite the promising nature of zero-shot translation, experts concede that it has yet to achieve the accuracy of conventional translation methods, where sentences are first rendered in an intermediary language before being translated into the target language.

The ramifications of neural machine translation transcend mere efficiency enhancements, as this breakthrough may expedite the extensive expansion of translation capabilities, accommodating an increasingly vast array of languages with unprecedented scalability. Kyunghyun Cho, a prominent researcher in the field, anticipates that a single neural model will soon be capable of translating between more than a hundred languages with minimal human intervention. With Google Translate already processing over 140 billion words per day, this technological advancement has the potential to revolutionize global communication by mitigating linguistic barriers more seamlessly than ever before. Nevertheless, while neural translation has demonstrated remarkable progress, it remains constrained by certain limitations that prevent it from fully replicating the expertise of a skilled human translator. Those who can read between the lines may recognize that, despite its sophistication, machine translation still lacks a crucial human element.

[I] Although artificial intelligence can proficiently handle straightforward translations, it continues to struggle with the complexities of linguistic nuance, cultural context, and stylistic variation. [II] A human translator possesses an intuitive grasp of idiomatic expressions, connotations, and domain-specific terminology, elements that remain elusive to machines. [III] According to Andrejs Vasiļjevs, a researcher specializing in neural translation for Baltic languages, future advancements must incorporate broader world knowledge and subject-specific expertise to refine interpretative accuracy. [IV]

(Adapted from New Scientist)

Question 31: Where in paragraph 4 does the following sentence best fit?

"Until neural translation systems can assimilate and apply such depth of understanding, human translators will remain indispensable for intricate, context-sensitive, and stylistically demanding translation tasks."

A. [I] B. [II] C. [III] D. [IV]