In late January 2025, DeepSeek, a Chinese artificial intelligence enterprise, came into the spotlight with its large - language models DeepSeek - R1 and DeepSeek - V3.

The models have attracted much attention due to their open - source nature. DeepSeek - R1 can be comparable to OpenAI's ChatGPT in performance, but with a much lower operating cost. The efficient training method and the improved Mixture of Experts (MoE) technology adopted by the model enable it to maintain efficient training even on low - end hardware. (The MoE technology allows different parts of the model, namely "experts", to specialize in specific tasks. During the inference process, only some "experts" are activated, thereby improving efficiency and potentially enhancing accuracy.)

Simone Bohnenberger - Rich, the Chief Product Officer of Phrase, believes that the high efficiency of DeepSeek models benefits from high - quality training data, highlighting the crucial role of data quality in artificial intelligence development.

Like most large - language models, DeepSeek's models have a wide range of practical applications, naturally including artificial intelligence translation.

User Feedback on Translation Capabilities

DeepSeek performs outstandingly in Chinese - to - English translation. Some users commented that compared with other models, "DeepSeek performs much better, which is not surprising."

Besides Chinese, DeepSeek also shows potential in translating many other languages. Its Serbian translation is not only accurate but also faster than that of ChatGPT. In basic Spanish translation tests, DeepSeek outperforms ChatGPT. It can not only provide accurate translations but also offer subsequent sentences, facilitating users' understanding without the need for additional information search.

In Turkish translation, the DeepSeek model has been praised for being able to well - conform to a company's style guide. Users of Czech and Hungarian languages said that when translating various texts, including legal documents and product introductions, its translation quality is as "excellent" as that of Claude.

For enterprises, using DeepSeek for translation has proven to be highly cost - effective. The CEO of a startup said, "Compared with Google Translate, DeepSeek has reduced our translation costs by 50x. So now we are integrating DeepSeek for French and German translations."

Cautious Attitudes of Language Industry Experts

Although many people are excited about DeepSeek's technological progress, language industry experts remain skeptical.

Gert van Assche, the CTO of Summa Linguae Technologies, acknowledges the logical reasoning ability of the R1 model but thinks that the translation performance of the V3 model is "rather disappointing." He emphasizes that more extensive testing is needed before drawing conclusions.

Konstantin Dranch, Language Industry Researcher and Founder at Custom.MT, agrees and calls on translators, Language Service Providers (LSPs), and localization teams to conduct systematic evaluations.

Yasmin Moslem, a natural language processing researcher, conducted medical translation tests of French and Portuguese using the DeepSeek - V3 model. Although the model performed well, Llama - 3.1 405B performed slightly better. In addition, fine - tuning NLLB 3.3B can also produce high - quality translation results, highlighting the effectiveness of professional machine translation models when there is sufficient domain - specific data.

For the translation industry, the development of artificial intelligence technologies like DeepSeek brings both opportunities and challenges.

AI translation can process a large amount of text in a very short time. For example, in news article translation, traditional human translation may take hours or even longer, while AI translation, with the help of powerful algorithms and computing capabilities, can be completed in just a few minutes, greatly improving translation efficiency and meeting the needs of rapid information dissemination. For enterprises, AI translation can significantly reduce costs. There is no need to pay high labor costs as in human translation. For enterprises that require a large amount of basic text translation, this can effectively save expenses and enable them to invest more resources in other business developments. AI translation, trained based on massive data, has outstanding performance in translating professional - field terms. When translating professional literature in medicine, law, science and technology, etc., it can quickly provide standard translations of terms, providing a basic reference for professionals.

However, AI translation also has some drawbacks. When dealing with complex semantics and cultural background knowledge, AI translation has obvious deficiencies. For example, in literary work translation, it often has difficulty accurately understanding and translating rhetorical devices such as metaphors and puns, resulting in translations that cannot convey the subtle connotations of the original text. AI translation has difficulty adjusting language styles according to different contexts, audiences, and purposes. Whether it is a formal business document or a casual social media copy, the results of AI translation often lack flexibility and are difficult to reflect the style differences of different styles of writing. When encountering ambiguous, non - standard, or ambiguous texts, AI translation has difficulty responding flexibly and cannot make accurate judgments and translations by combining context and common sense like human translation.

About Glodom

In this context, Glodom adopts the MTPE (Machine Translation Post - Editing) model. This model skillfully combines the efficiency of machine translation and the precision of human post - editing. Post - editing personnel can correct common grammatical errors in machine translation and optimize sentence expressions to make them more in line with the usage and style of the target language. In professional - field translations, post - editing personnel can ensure the accuracy of terms with their professional knowledge, avoiding serious consequences caused by incorrect translations. At the same time, they can also adjust translations according to specific customer requirements, such as brand styles and audience characteristics, providing high - quality translation services that better meet customer needs.

Glodom has a positive and prudent attitude towards the MTPE model. The positivity lies in our full recognition of the great potential of the MTPE model in improving translation efficiency and reducing costs. It can help us respond more quickly to customer needs and handle a large number of translation tasks. The prudence is reflected in our awareness that machine translation still has certain limitations at present. Therefore, we strictly control the quality of the post - editing link. We have a professional post - editing team who not only have excellent language skills but also have in - depth knowledge of various professional fields. Through continuous training and quality monitoring, we ensure that every translation can meet high - quality standards and provide customers with high - quality and reliable translation services.

Original article link:Experts Weigh In on DeepSeek AI Translation Quality - Slator

Article source:Slator

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