36 ms·

Playing Around with Machine Translation

31 points by Thevet 3y ago

edgarvaldes 3y ago
Google Translate and Google Search have a preference for acronyms. I translate a lot of subtitles using GT, and every time a characters asks "Who?" GT gives me the version for "World Health Organization?" If a character is named "Mia", GT gives the hilarious "Missing in action", etc. Still, the combo between WhisperX, Google Translate and Subtitle Edit are the Holy Grail I dream of just a year ago.
- vintermann 3y ago
  With very little context, that might happen. You're not translating the subtitles line by line, are you? Either way you're probably much better off using chatgpt.
  - edgarvaldes 3y ago
    >You're not translating the subtitles line by line, are you? Subtitle Edit has a built-in function that calls the GT API.
famouswaffles 3y ago
It's weirdly flown under the radar but GPT style models as translators are a lot better than state of the art Machine Translators (Deepl, NLLB, Google etc). Like above, you can already see the difference with close language pairs where Google etc are already very good. For pairs like English and Japanese etc, google et al will happily devolve into half gibberish so the difference is even more stark. I did a number of examples a couple months back with English/Chinese before 4 was released. Even then you can see it's a lot better and 4 is as usual a lot better than 3.5. https://github.com/ogkalu2/Human-parity-on-machine-translations https://github.com/ogkalu2/Human-parity-on-machine-translati...
- luxpir 3y ago
  It's not under the radar because that's sadly not the case, although it looks like it should work better on the surface. Neural machine translation is just more consistent, doesn't hallucinate and can be easily and cheaply trained over time. They have some benefits, but as a lot of LLM research has found, they are not production ready. Yet.
  - deleted 3y ago
    [deleted]
  - famouswaffles 3y ago
    >It's not under the radar because that's sadly not the case It is the case. Haven't seen anyone who uses both who thinks otherwise. also https://arxiv.org/abs/2301.13294 https://arxiv.org/abs/2301.13294. This benchmarks 3.5 which is quite a bit worse than 4 as it is against Google, NLLB, Deepl here https://arxiv.org/pdf/2304.02210.pdf https://arxiv.org/pdf/2304.02210.pdf, GPT wins overwhelmingly with human evaluations. Seems like the typically evaluation models aren't really cutting it anymore especially BLEU >Neural machine translation is just more consistent, doesn't hallucinate It's not more consistent. The 2nd bit is just wrong lol. One of the biggest complaints of Deepl is the tendency to make stuff up to make translations seem more natural. Summarization and Translation are the tasks GPT models hallucinate the least.
    - cj 3y ago
      I wrote a much longer reply but looks like you deleted the downvoted comment and reposted it. > It's not more consistent. I think the parent was probably saying for a given input, Google MT provides the same output. What is the value of temperature/variability in a LLM powered MT model? I’d assume given the same inputs, you should only be given the best output.
      - famouswaffles 3y ago
        >I think the parent was probably saying for a given input, Google MT provides the same output. I don't care about being given the exact same output (you're not getting deterministic translations from people either). I care about quality translations. Variability for GPT style translations is much more about word choice and style than wrong or extremely different translations. and if i really wanted to, i could guide both (word choice, style) either with instructions or examples.
l0new0lf-G 3y ago
Don't forget that English and French have much in common lexilogically and grammatically, and even some slang must be similar because of geographical proximity and cultural exchange. I am nearly certain that no machine will ever be able to accurately translate between languages with significant linguistic distance (e.g. Japanese and Swedish). I experience this first hand, whenever I translate from my native Greek to English, especially if there is slang involved. Whenever Google translate encounters long phrases in my texts, the result is comical, not to mention that the emotions are not properly conveyed. I can only begin to imagine the inaccuracies in translations from Mandarin. Nevertheless, I never expected even fairly accurate translations between even related languages such as French and English. It indeed sends chills down the spine. It feels like there is some form of actual intelligence involved.
- tralarpa 3y ago
  > I am nearly certain that no machine will ever be able to accurately translate between languages with significant linguistic distance (e.g. Japanese and Swedish). Is that really the reason? Or rather the fact that there is much less training data available?
  - naniwaduni 3y ago
    Languages even moderately distant tend to strain the concept of an accurate translation in the first place for any nontrivial utterance.
    - bugglebeetle 3y ago
      Eh, I speak both English and Japanese and I would say that what counts as accurate translation is what is most proximate to that threshold of fundamental dissimilarity. Measuring translation accuracy for all languages the same way is more the problem here. There is no such thing as 1-1 to translation. It’s more like 1-(1+n) where n accounts for said distance. For languages with shared origins, n can be fairly small, while for those with entirely separate ones, it can be quite large. That being said, Japanese to English translation in things like popular culture tends to take far too many liberties, I expect because the culture around Japanese translation in America has a very annoying, Orientalist bent, with people getting off on their “expertise” about a fake exoticism.
ZeroGravitas 3y ago
I wonder if anyone at Project Gutenberg or similar is looking to autogenerate translations of out of copyright classics. Ice seen a few people here recommend against some copyright free translations because a more recent translation is better. Possibly the AI tools aren't yet as good as the best human translators, but are they already better than what's available copyright free?
- OkayPhysicist 3y ago
  Fundamentally, translation is a harder problem than it gets credit for. At the dictionary level, you're mostly alright, as words and concepts tend to have pretty close direct translations (though you'll often need context, as homynyms exist). A step above that, forming full statements, you run into some difficulty, due to the difficulty in translating the subtleties of word choice, as you've got a fair amount of cultural context to take into account. "Forgive me Father for I have sinned" does not carry the connotations as "Sorry, Daddy, I've been naughty". But a step up from there, translating entire texts, you need a pretty complex theory of the mind, as you need to take into account the context of the author's perspective, their interpretation of their intended audience's perspective, and your audience's perspective. For example, my "Forgive/Sorry" joke earlier relies on the context of both you and I being somewhat aware of A) the Catholic church's process of confessional and B) the relatively modern use of "Daddy" and "naughty" with sexual connotations, which, forget language specific, is culturally specific, where the joke would break if you tried telling it to, say, a place that still used "Daddy" as perfectly normal way to address one's father, or lacked the cultural norm of sexualizing authority. If someone was trying to translate this comment to another language, they might have to completely alter that joke in order for it to make any sense, at which point that entire last paragraph would have to change, etc. Modern AI tools have largely reached a point where the homonym problem isn't crippling them anymore, but haven't really reached much beyond that.
- vintermann 3y ago
  There's no point in offering people something they can easily make themselves. Especially when it comes to machine translation. I often see YouTube videos with gratingly bad Norwegian titles. I believe YouTube upranks videos if the creator provides translations, and plenty of sleazy video creators just machine translate their titles to umpteen languages. Since it's a low-context translation, just one random line pasted into GT with the translator having no idea what it's about, even the best machine translation in the world will frequently produce junk. And I have to translate the junk back into English to understand what the title was probably supposed to be. It does not help me. (By incentivizing this junk, Google is probably poisoning its own translation training data, but that's another matter). So two hard rules should be 1. always leave up to the user whether to use machine translation or not 2. always clearly mark translated text, especially machine translated. Gutenberg should not waste space hosting machine translated works, unless they put a lot of effort into ensuring the quality of the translations, effort that the user couldn't easily replicate.
tkgally 3y ago
I worked as a freelance Japanese-to-English translator for twenty years, until 2005. After a break in academia, I am now semiretired, doing translation part-time and following closely developments in machine translation and in AI in general. Two advantages of LLM-based translation are that it is promptable and nondeterministic. While Google Translate, DeepL, etc. will translate a text the same way every time, LLMs let you specify the purpose and desired style of the translation, and you can ask for multiple translations of the same text. Lately, I’ve been translating some speeches from Japanese to English. While I used to translate such texts by myself, now I first run them through LLMs (Claude 2 and GPT-4) to get three or four different English versions. I refer to those MT versions as well as the Japanese original while I prepare my own draft. This has two advantages for me: the multiple translations often give me ideas for English expressions that would not have occurred to me on my own, and I am able to devote more of my energy to polishing the final versions. Another way I use LLMs is as a thesaurus on overdrive. If I have a Japanese expression that I understand but can’t think of a good way to express in English—this happens a lot; that’s why translation is difficult—I will give the expression to GPT-4, explain the purpose of the text, provide additional context if necessary, and ask for ten English renditions of the expression. There will almost always be one or two versions that I like. Here is an example of ten English translations by GPT-4 of the same Japanese text: https://www.gally.net/temp/20230915pmtranslations.html https://www.gally.net/temp/20230915pmtranslations.html Several months ago, I made some videos about ChatGPT and translation, if anyone is interested: https://www.youtube.com/@Tom_Gally_UTokyo https://www.youtube.com/@Tom_Gally_UTokyo
- qingcharles 3y ago
  This is fascinating, as it mirrors how I use GPT to code. I generally use it to find a solution, that is often not quite accurate, but opens a wide range of ideas that I often would not have come up with myself. サンキュー! (* ^ ω ^)
bagasme 3y ago
Another case that scratches my itch was when I take a look on Spanish Linux-themed sites with Indonesian translation (as appeared on first page of Google search results). The translated text looks like stiff, gibberish/incomprehensible, and too formal. It reminds me of statistical machine translation (SMT) output, or is the text really MT-generated? Examples: [1], [2], and [3]. On the side note, all these sites have a similar design and layout, which makes me wonder if these come from the same organization/group or not. And even these sites' title is also translated, which further raise the alarm. End result? Here be dragons: visit these sites with a grain of salt. [1]: https://blog.desdelinux.net/id/gentoo-sources-arm-kernel-tanpa-usaha-mati/ https://blog.desdelinux.net/id/gentoo-sources-arm-kernel-tan... [2]: https://ubunlog.com/id/linux-6-6-rc1-sekarang-tersedia-dengan-kabar-baik-tetapi-tanpa-bcachefs/ https://ubunlog.com/id/linux-6-6-rc1-sekarang-tersedia-denga... [3]: https://www.linuxadictos.com/id/setelah-memasang-fedora-26.html https://www.linuxadictos.com/id/setelah-memasang-fedora-26.h...
- qingcharles 3y ago
  The design smells of blog spam, AI generated. I can't read the text so I can't tell from there.
  - bagasme 3y ago
    That's what I think. I guess that contributors on these sites just pull in raw MT-generated translations and post them as is, or do these sites really have human Spanish/Indonesian translators that do actual proofreading? I bet they have none. Take an example of desdelinux case. The original article (in Spanish) reads: ``` Bueno, como es de esperarse en Gentoo, existen muchas opciones de kernel, les dejo aquí una pequeña lista para que vean mejor a lo que me refiero: gentoo-sources: Kernel 4.12 con parches especiales para Gentoo Linux. git-sources: Kernel directamente descargado desde el repositorio Git de Linus. vanilla-sources: Kernel completo sin ningún tipo de parche. ``` which is translated on the site as: ``` Seperti yang diharapkan di Gentoo, ada banyak opsi kernel, saya tinggalkan daftar kecil di sini agar Anda dapat memahami dengan lebih baik apa yang saya maksud: sumber gentoo: Kernel 4.12 dengan patch khusus untuk Gentoo Linux. git-sumber: Kernel langsung diunduh dari gudang Linus Git. vanilla-sumbersource: Kernel penuh tanpa patch apapun. ``` Notice that linux sources package names (gentoo-sources, git-sources, and vanilla-sources) are translated despite being proper names, which should not be in this case. Whereas Google Translate renders the translated passage as: ``` Seperti yang diharapkan di Gentoo, ada banyak opsi kernel, berikut adalah daftar kecilnya sehingga Anda dapat lebih memahami maksud saya: gentoo-sources: Kernel 4.12 dengan patch khusus untuk Gentoo Linux. git-sources: Kernel diunduh langsung dari repositori Git Linus. vanilla-sources: Kernel lengkap tanpa patch apa pun. ``` For more natural and flowing translation, I'd like to write it instead as: ``` Seperti yang diharapkan, ada banyak pilihan kode sumber kernel di Gentoo. Beberapa diantaranya: gentoo-sources: Kernel versi 4.12 dengan tambalan spesifik untuk Gentoo. git-sources: Kernel dengan potret langsung dari repositori Linus. vanilla-sources: Rilis kernel asli tanpa tambalan apapun. ``` These intricacies above reminds me of biblical translation context. Translation can be done in either formal equivalence (word-by-word like NASB and ESV) or dynamic equivalence (thought-by-thought as in NIV and NLT). The former seeks to stay close to original language's wording, but may produce stiff target language text. The latter tries to convey meaning of original language in more readable target language text by making use of context interpretation, which if it is done poorly, can divert actual meaning from the original. Thanks for the reply.
YeGoblynQueenne 3y ago
>> Even ten years ago, I would never have dreamed that we would have such accurate machine translation so soon. We already did. I did my MSc in 2014 and, in an Natural Language Understanding course we talked about machine translation and looked at some results of translating between English and French. It was difficult to find any fault with the translation. Machine translation capabilities haven't changed much since then. And I remember Microsoft advertising the bright future of kids without a common language using Skype (!) to communicate and play together, something that still isn't possible. Well, certainly not with Skype.
- famouswaffles 3y ago
  >about machine translation and looked at some results of translating between English and French. It was difficult to find any fault with the translation. This has never been the case for distant language pairs till GPT. That is a pretty big change. https://youtu.be/5KKDCp3OaMo?si=uVEAK72qHqXsB4Jo https://youtu.be/5KKDCp3OaMo?si=uVEAK72qHqXsB4Jo

31 points by Thevet 3y ago

Google Translate and Google Search have a preference for acronyms. I translate a lot of subtitles using GT, and every time a characters asks "Who?" GT gives me the version for "World Health Organization?" If a character is named "Mia", GT gives the hilarious "Missing in action", etc. Still, the combo between WhisperX, Google Translate and Subtitle Edit are the Holy Grail I dream of just a year ago.

vintermann 3y ago
With very little context, that might happen. You're not translating the subtitles line by line, are you? Either way you're probably much better off using chatgpt.
- edgarvaldes 3y ago
  >You're not translating the subtitles line by line, are you? Subtitle Edit has a built-in function that calls the GT API.

famouswaffles 3y ago

It's weirdly flown under the radar but GPT style models as translators are a lot better than state of the art Machine Translators (Deepl, NLLB, Google etc). Like above, you can already see the difference with close language pairs where Google etc are already very good. For pairs like English and Japanese etc, google et al will happily devolve into half gibberish so the difference is even more stark. I did a number of examples a couple months back with English/Chinese before 4 was released. Even then you can see it's a lot better and 4 is as usual a lot better than 3.5. https://github.com/ogkalu2/Human-parity-on-machine-translations https://github.com/ogkalu2/Human-parity-on-machine-translati...

luxpir 3y ago
It's not under the radar because that's sadly not the case, although it looks like it should work better on the surface. Neural machine translation is just more consistent, doesn't hallucinate and can be easily and cheaply trained over time. They have some benefits, but as a lot of LLM research has found, they are not production ready. Yet.
- deleted 3y ago
  [deleted]
- famouswaffles 3y ago
  >It's not under the radar because that's sadly not the case It is the case. Haven't seen anyone who uses both who thinks otherwise. also https://arxiv.org/abs/2301.13294 https://arxiv.org/abs/2301.13294. This benchmarks 3.5 which is quite a bit worse than 4 as it is against Google, NLLB, Deepl here https://arxiv.org/pdf/2304.02210.pdf https://arxiv.org/pdf/2304.02210.pdf, GPT wins overwhelmingly with human evaluations. Seems like the typically evaluation models aren't really cutting it anymore especially BLEU >Neural machine translation is just more consistent, doesn't hallucinate It's not more consistent. The 2nd bit is just wrong lol. One of the biggest complaints of Deepl is the tendency to make stuff up to make translations seem more natural. Summarization and Translation are the tasks GPT models hallucinate the least.
  - cj 3y ago
    I wrote a much longer reply but looks like you deleted the downvoted comment and reposted it. > It's not more consistent. I think the parent was probably saying for a given input, Google MT provides the same output. What is the value of temperature/variability in a LLM powered MT model? I’d assume given the same inputs, you should only be given the best output.
    - famouswaffles 3y ago
      >I think the parent was probably saying for a given input, Google MT provides the same output. I don't care about being given the exact same output (you're not getting deterministic translations from people either). I care about quality translations. Variability for GPT style translations is much more about word choice and style than wrong or extremely different translations. and if i really wanted to, i could guide both (word choice, style) either with instructions or examples.

l0new0lf-G 3y ago

Don't forget that English and French have much in common lexilogically and grammatically, and even some slang must be similar because of geographical proximity and cultural exchange. I am nearly certain that no machine will ever be able to accurately translate between languages with significant linguistic distance (e.g. Japanese and Swedish). I experience this first hand, whenever I translate from my native Greek to English, especially if there is slang involved. Whenever Google translate encounters long phrases in my texts, the result is comical, not to mention that the emotions are not properly conveyed. I can only begin to imagine the inaccuracies in translations from Mandarin. Nevertheless, I never expected even fairly accurate translations between even related languages such as French and English. It indeed sends chills down the spine. It feels like there is some form of actual intelligence involved.

tralarpa 3y ago
> I am nearly certain that no machine will ever be able to accurately translate between languages with significant linguistic distance (e.g. Japanese and Swedish). Is that really the reason? Or rather the fact that there is much less training data available?
- naniwaduni 3y ago
  Languages even moderately distant tend to strain the concept of an accurate translation in the first place for any nontrivial utterance.
  - bugglebeetle 3y ago
    Eh, I speak both English and Japanese and I would say that what counts as accurate translation is what is most proximate to that threshold of fundamental dissimilarity. Measuring translation accuracy for all languages the same way is more the problem here. There is no such thing as 1-1 to translation. It’s more like 1-(1+n) where n accounts for said distance. For languages with shared origins, n can be fairly small, while for those with entirely separate ones, it can be quite large. That being said, Japanese to English translation in things like popular culture tends to take far too many liberties, I expect because the culture around Japanese translation in America has a very annoying, Orientalist bent, with people getting off on their “expertise” about a fake exoticism.

ZeroGravitas 3y ago

I wonder if anyone at Project Gutenberg or similar is looking to autogenerate translations of out of copyright classics. Ice seen a few people here recommend against some copyright free translations because a more recent translation is better. Possibly the AI tools aren't yet as good as the best human translators, but are they already better than what's available copyright free?

OkayPhysicist 3y ago
Fundamentally, translation is a harder problem than it gets credit for. At the dictionary level, you're mostly alright, as words and concepts tend to have pretty close direct translations (though you'll often need context, as homynyms exist). A step above that, forming full statements, you run into some difficulty, due to the difficulty in translating the subtleties of word choice, as you've got a fair amount of cultural context to take into account. "Forgive me Father for I have sinned" does not carry the connotations as "Sorry, Daddy, I've been naughty". But a step up from there, translating entire texts, you need a pretty complex theory of the mind, as you need to take into account the context of the author's perspective, their interpretation of their intended audience's perspective, and your audience's perspective. For example, my "Forgive/Sorry" joke earlier relies on the context of both you and I being somewhat aware of A) the Catholic church's process of confessional and B) the relatively modern use of "Daddy" and "naughty" with sexual connotations, which, forget language specific, is culturally specific, where the joke would break if you tried telling it to, say, a place that still used "Daddy" as perfectly normal way to address one's father, or lacked the cultural norm of sexualizing authority. If someone was trying to translate this comment to another language, they might have to completely alter that joke in order for it to make any sense, at which point that entire last paragraph would have to change, etc. Modern AI tools have largely reached a point where the homonym problem isn't crippling them anymore, but haven't really reached much beyond that.
vintermann 3y ago
There's no point in offering people something they can easily make themselves. Especially when it comes to machine translation. I often see YouTube videos with gratingly bad Norwegian titles. I believe YouTube upranks videos if the creator provides translations, and plenty of sleazy video creators just machine translate their titles to umpteen languages. Since it's a low-context translation, just one random line pasted into GT with the translator having no idea what it's about, even the best machine translation in the world will frequently produce junk. And I have to translate the junk back into English to understand what the title was probably supposed to be. It does not help me. (By incentivizing this junk, Google is probably poisoning its own translation training data, but that's another matter). So two hard rules should be 1. always leave up to the user whether to use machine translation or not 2. always clearly mark translated text, especially machine translated. Gutenberg should not waste space hosting machine translated works, unless they put a lot of effort into ensuring the quality of the translations, effort that the user couldn't easily replicate.

tkgally 3y ago

I worked as a freelance Japanese-to-English translator for twenty years, until 2005. After a break in academia, I am now semiretired, doing translation part-time and following closely developments in machine translation and in AI in general. Two advantages of LLM-based translation are that it is promptable and nondeterministic. While Google Translate, DeepL, etc. will translate a text the same way every time, LLMs let you specify the purpose and desired style of the translation, and you can ask for multiple translations of the same text. Lately, I’ve been translating some speeches from Japanese to English. While I used to translate such texts by myself, now I first run them through LLMs (Claude 2 and GPT-4) to get three or four different English versions. I refer to those MT versions as well as the Japanese original while I prepare my own draft. This has two advantages for me: the multiple translations often give me ideas for English expressions that would not have occurred to me on my own, and I am able to devote more of my energy to polishing the final versions. Another way I use LLMs is as a thesaurus on overdrive. If I have a Japanese expression that I understand but can’t think of a good way to express in English—this happens a lot; that’s why translation is difficult—I will give the expression to GPT-4, explain the purpose of the text, provide additional context if necessary, and ask for ten English renditions of the expression. There will almost always be one or two versions that I like. Here is an example of ten English translations by GPT-4 of the same Japanese text: https://www.gally.net/temp/20230915pmtranslations.html https://www.gally.net/temp/20230915pmtranslations.html Several months ago, I made some videos about ChatGPT and translation, if anyone is interested: https://www.youtube.com/@Tom_Gally_UTokyo https://www.youtube.com/@Tom_Gally_UTokyo

qingcharles 3y ago
This is fascinating, as it mirrors how I use GPT to code. I generally use it to find a solution, that is often not quite accurate, but opens a wide range of ideas that I often would not have come up with myself. サンキュー! (* ^ ω ^)

bagasme 3y ago

Another case that scratches my itch was when I take a look on Spanish Linux-themed sites with Indonesian translation (as appeared on first page of Google search results). The translated text looks like stiff, gibberish/incomprehensible, and too formal. It reminds me of statistical machine translation (SMT) output, or is the text really MT-generated? Examples: [1], [2], and [3]. On the side note, all these sites have a similar design and layout, which makes me wonder if these come from the same organization/group or not. And even these sites' title is also translated, which further raise the alarm. End result? Here be dragons: visit these sites with a grain of salt. [1]: https://blog.desdelinux.net/id/gentoo-sources-arm-kernel-tanpa-usaha-mati/ https://blog.desdelinux.net/id/gentoo-sources-arm-kernel-tan... [2]: https://ubunlog.com/id/linux-6-6-rc1-sekarang-tersedia-dengan-kabar-baik-tetapi-tanpa-bcachefs/ https://ubunlog.com/id/linux-6-6-rc1-sekarang-tersedia-denga... [3]: https://www.linuxadictos.com/id/setelah-memasang-fedora-26.html https://www.linuxadictos.com/id/setelah-memasang-fedora-26.h...

qingcharles 3y ago
The design smells of blog spam, AI generated. I can't read the text so I can't tell from there.
- bagasme 3y ago
  That's what I think. I guess that contributors on these sites just pull in raw MT-generated translations and post them as is, or do these sites really have human Spanish/Indonesian translators that do actual proofreading? I bet they have none. Take an example of desdelinux case. The original article (in Spanish) reads: ``` Bueno, como es de esperarse en Gentoo, existen muchas opciones de kernel, les dejo aquí una pequeña lista para que vean mejor a lo que me refiero: gentoo-sources: Kernel 4.12 con parches especiales para Gentoo Linux. git-sources: Kernel directamente descargado desde el repositorio Git de Linus. vanilla-sources: Kernel completo sin ningún tipo de parche. ``` which is translated on the site as: ``` Seperti yang diharapkan di Gentoo, ada banyak opsi kernel, saya tinggalkan daftar kecil di sini agar Anda dapat memahami dengan lebih baik apa yang saya maksud: sumber gentoo: Kernel 4.12 dengan patch khusus untuk Gentoo Linux. git-sumber: Kernel langsung diunduh dari gudang Linus Git. vanilla-sumbersource: Kernel penuh tanpa patch apapun. ``` Notice that linux sources package names (gentoo-sources, git-sources, and vanilla-sources) are translated despite being proper names, which should not be in this case. Whereas Google Translate renders the translated passage as: ``` Seperti yang diharapkan di Gentoo, ada banyak opsi kernel, berikut adalah daftar kecilnya sehingga Anda dapat lebih memahami maksud saya: gentoo-sources: Kernel 4.12 dengan patch khusus untuk Gentoo Linux. git-sources: Kernel diunduh langsung dari repositori Git Linus. vanilla-sources: Kernel lengkap tanpa patch apa pun. ``` For more natural and flowing translation, I'd like to write it instead as: ``` Seperti yang diharapkan, ada banyak pilihan kode sumber kernel di Gentoo. Beberapa diantaranya: gentoo-sources: Kernel versi 4.12 dengan tambalan spesifik untuk Gentoo. git-sources: Kernel dengan potret langsung dari repositori Linus. vanilla-sources: Rilis kernel asli tanpa tambalan apapun. ``` These intricacies above reminds me of biblical translation context. Translation can be done in either formal equivalence (word-by-word like NASB and ESV) or dynamic equivalence (thought-by-thought as in NIV and NLT). The former seeks to stay close to original language's wording, but may produce stiff target language text. The latter tries to convey meaning of original language in more readable target language text by making use of context interpretation, which if it is done poorly, can divert actual meaning from the original. Thanks for the reply.

YeGoblynQueenne 3y ago

>> Even ten years ago, I would never have dreamed that we would have such accurate machine translation so soon. We already did. I did my MSc in 2014 and, in an Natural Language Understanding course we talked about machine translation and looked at some results of translating between English and French. It was difficult to find any fault with the translation. Machine translation capabilities haven't changed much since then. And I remember Microsoft advertising the bright future of kids without a common language using Skype (!) to communicate and play together, something that still isn't possible. Well, certainly not with Skype.

famouswaffles 3y ago
>about machine translation and looked at some results of translating between English and French. It was difficult to find any fault with the translation. This has never been the case for distant language pairs till GPT. That is a pretty big change. https://youtu.be/5KKDCp3OaMo?si=uVEAK72qHqXsB4Jo https://youtu.be/5KKDCp3OaMo?si=uVEAK72qHqXsB4Jo