Language diversity is a beautiful aspect of humanity, as it reflects the wide range of cultures and experiences that make up the world. But let’s be real. It also becomes a barrier at some point, especially when speaking to someone with a different tongue.
But thanks to machine translators, language barrier is no longer that big of an issue. You can instantly understand messages, express ideas effectively, and build relationships with other cultures. Perhaps some of the translation platforms you probably have encountered include Google Translate, DeepL, and today’s rising AI called ChatGPT.
Let’s put the three to the test to see which one is the ultimate translator.
The World’s Top Translation Programs
Google translate, a web-based tool, uses Neural Machine Translation (GNMT) technology. With the help of GNMT, it can generate outputs similar to those produced by a human.
The revolutionary AI, ChatGPT, also translates text. It’s a large language model (LLM) that responds to prompts by processing massive amounts of data. This input becomes the tool’s basis for predicting an output aligned with the user’s instructions.
Last but definitely not least is DeepL. This translator tool aims to stay ahead of the game by improving the overall neural network methodology, focusing on four aspects: network architecture, training data, process, and size. As it claims, its advanced AI technology has better translation accuracy than others.
These text translation tools have proven to help bridge communication gaps. However, each performs differently, which may impose difficulties instead of ease on the one using them. Thankfully, u/Fit-Meet1359, a Reddit user, tests them to see which AI can win this ultimate translator showdown.
Experiment 1: Translating a French Joke to English
u/Fit-Meet1359 found a year-old joke that was originally posted on a French subreddit, which was later on used to test the translation capabilities of Google Translate, ChatGPT, and DeepL.
She first ran the joke on Google Translate. Several mistakes popped up when she read the output, including awkward word placement and usage, incorrect punctuation, as well as inconsistent verb tenses.
u/Fit-Meet1359 was impressed with ChatGPT’s result, as it came out better. The exact prompt she used was: “The following is a joke written in French. Rewrite it as though it were originally written by a native English speaker:”
Obviously, the prompt allowed the tool to give a much more aligned result to the user’s liking, which Google Translate can’t currently do. Although the output has mistakes, such as unnecessary word repetitions, it’s only minimal and not outright.
Lastly, DeepL also showed awkward word choices and misuse of idioms. However, there are only slight mistakes in utilizing punctuation.
Overall, ChatGPT performed best in translating jokes. Fit-Meet1359 gave it a score of three, while DeepL got two and Google Translate acquired one.
Experiment 2: A Tweet about COVID Isolation
u/Fit-Meet1359’s next experiment involved a tweet from a Chinese woman, who talked about her sentiment about being isolated due to COVID.
It seems that Google Translate is completely oblivious to the fact that “the new crown” actually meant COVID-19. Not only that, but the tool also had trouble understanding where one sentence ends and another begins.
For ChatGPT, u/Fit-Meet1359 used this exact prompt to translate the post: “The following is a social media post by a Chinese woman. Please rewrite it as though it were a social media post written by a native English speaker.”
According to her, the translation tool failed to understand that the author was placed in an isolation room even without having symptoms. In this case, there was a minimal change in the overall context.
Looks like DeepL is also having a rough time with the Chinese social media post. u/Fit-Meet1359 thinks it could probably be because Mandarin is one of its weaknesses. The main challenge for the tool was about grasping the author’s writing style and connecting the ideas into cohesive and coherent sentences.
The best machine translator in this aspect is evident, with ChatGPT ranking on top, while Google Translate came second and DeepL on the third spot.
Experiment 3: A Russian Soldier’s Telegram Post
u/Fit-Meet1359’s last translation experiment was about a Telegram post from a Russian soldier battling in Ukraine.
Google Translate’s performance in translating the text was quite good according to u/Fit-Meet1359. However, she worries about the tool’s last mistake, as it opens the possibility for misunderstandings.
Moving on, she used this prompt to ask ChatGPT to translate the Telegram post into English: “The following is a social media post by a Russian soldier who is fighting in Ukraine. Rewrite it as though the author were a native English speaker.”
ChatGPT is also advanced in this experiment, as you can ask the prompt to clarify unusual terms, which she did. She then typed another prompt: “Can you explain the meaning of “Point U,” providing the original Russian word as well as a transliteration?” However, it tends to go overboard in terms that would’ve been better to translate simpler.
u/Fit-Meet1359 also thinks that DeepL performed as great as ChatGPT. According to her, it gave a better translation to the term “Tochka U.”
In this aspect, ChatGPT and DeepL scored equally, while Google Translate had to keep up due to the possible misunderstandings that may arise with its output.
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The Ultimate Showdown Winner
In the experiments, it’s clear that ChatGPT outperforms Google Translate and DeepL when it comes to translation due to its superior ability to detect nuance and produce cohesive and coherent translations. That said, ChatGPT also has a tendency to use a greater number of words, sometimes leading to unnecessarily lengthy translations.
You can also try spicing up translations by exploring prompts like “If this were written by a native English speaker…” These instructions can lead to more natural-sounding translations and help ChatGPT avoid a “translationese” writing style.
When crafting prompts, provide ChatGPT with as much context as possible about the text and its author for more accuracy. However, it’s important to note that these insights were from her perspective, and outcomes may vary per user.
It may be possible that ChatGPT is only superior in some languages but falls short in others. This is because each model was trained on different datasets, so their strengths likely vary.
This only shows that there’s a lot more room to explore, especially since these machine translators are constantly evolving and improving. From translating complex languages to breaking down barriers of communication, choosing the right translation tool has the power to make a significant impact.
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