Show HN: Neural Japanese Transliteration
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Show HN: Neural Japanese Transliteration
1–10 of 55 posts
Re: Show HN: Neural Japanese Transliteration
#2Awesome! Where can I get this keyboard?
Re: Show HN: Neural Japanese Transliteration
#3Cool! So, would it be correct to say that this is in essence generates a disambiguation model for a language with a lot of homonyms due to having relatively few sounds (but also with some variation in morpheme boundaries, e.g. "an-i" vs "ani")?
Re: Show HN: Neural Japanese Transliteration
#4Interesting project! Does anyone know what iOS is using for it Japanese transliteration predictions? Mine worked well for a long time, but in the past 3 months it's gone haywire for common kanji suggestions. The other day it had "機能" as the first/only suggestion for "きのう," and I had to dig down into the menu to arrive at the intended, "昨日." Had a lot of similar experiences recently.
Re: Show HN: Neural Japanese Transliteration
#5Interesting project! Does anyone know what iOS is using for it Japanese transliteration predictions? Mine worked well for a long time, but in the past 3 months it's gone haywire for common kanji suggestions. The other day it had "機能" as the first/only suggestion for "きのう," and I had to dig down into the menu to arrive at the intended, "昨日." Had a lot of similar experiences recently.
Try resetting your keyboard under Settings>General>Reset
Re: Show HN: Neural Japanese Transliteration
#6Cool! So, would it be correct to say that this is in essence generates a disambiguation model for a language with a lot of homonyms due to having relatively few sounds (but also with some variation in morpheme boundaries, e.g. "an-i" vs "ani")?
For what it's worth, this is basically just the same as any popular Japanese (or Chinese) input method. Usually the approach is to greedily form the smallest set of the longest words from the given syllables, because people tend to give inputs where all the words are complete. Sometimes people use markov models to fix situations where that falls over.
Not sure how well this model performs, but the task is not novel.
P.S. Mandarin has a fair few homophones as well (yes, tones and all). English has tonnes of phonemes, and still we have loads of homophones (and in some of the most common words, no less!). Japanese, to my elementary-level ear, doesn't sound an order of magnitude more ambiguous than English.
Re: Show HN: Neural Japanese Transliteration
#7Interesting project! Does anyone know what iOS is using for it Japanese transliteration predictions? Mine worked well for a long time, but in the past 3 months it's gone haywire for common kanji suggestions. The other day it had "機能" as the first/only suggestion for "きのう," and I had to dig down into the menu to arrive at the intended, "昨日." Had a lot of similar experiences recently.
Does it act up on your desktop OS as well? I know there's an alternative from the ATOK developers for iOS (http://www.justsystems.com/jp/products/atok_ios/) but I haven't tried that one.
Re: Show HN: Neural Japanese Transliteration
#8Interesting project! Does anyone know what iOS is using for it Japanese transliteration predictions? Mine worked well for a long time, but in the past 3 months it's gone haywire for common kanji suggestions. The other day it had "機能" as the first/only suggestion for "きのう," and I had to dig down into the menu to arrive at the intended, "昨日." Had a lot of similar experiences recently.
You can reset keyboard dictionary in iOS settings, maybe doing that will help.
Re: Show HN: Neural Japanese Transliteration
#9Cool! So, would it be correct to say that this is in essence generates a disambiguation model for a language with a lot of homonyms due to having relatively few sounds (but also with some variation in morpheme boundaries, e.g. "an-i" vs "ani")?
For what it's worth, this is basically just the same as any popular Japanese (or Chinese) input method. Usually the approach is to greedily form the smallest set of the longest words from the given syllables, because people tend to give inputs where all the words are complete. Sometimes people use markov models to fix situations where that falls over. Not sure how well this model performs, but the task is not novel.…
Well, to be honest my first impression was that this is what I've been using all along. I just liked that someone experimented a bit. :) Perhaps I assumed this would end up with a different predictive model. No idea what kind of corpora have been used for the input methods already available.
Edit: your P.S. made me remember the "ma-ma-ma-ma..." mouthful the Chinese language students I studied in parallel with discovered (apparently well-known - something about a horse and a...mother?). If tones are not represented in romanised Chinese, things seem to get tricky, indeed.
Re: Show HN: Neural Japanese Transliteration
#10Earlier quoted context omitted.
For what it's worth, this is basically just the same as any popular Japanese (or Chinese) input method. Usually the approach is to greedily form the smallest set of the longest words from the given syllables, because people tend to give inputs where all the words are complete. Sometimes people use markov models to fix situations where that falls over. Not sure how well this model performs, but the task is not novel.…
Well, to be honest my first impression was that this is what I've been using all along. I just liked that someone experimented a bit. :) Perhaps I assumed this would end up with a different predictive model. No idea what kind of corpora have been used for the input methods already available. Edit: your P.S. made me remember the "ma-ma-ma-ma..." mouthful the Chinese language students I studied in parallel with discove…
For what it's worth, there's a lot of work to do on Japanese input methods. To a human it may be obvious that hawotogu should become 刃を研ぐ (sharpen a/the blade), and that one does not typically sharpen or hone a 歯 (tooth), 葉(leaf), 派(party/ingroup), or even 覇(~hegemony); but to a computer without topic-specific context, each of these is equally valid. One might be thankful that humans don't really say all that many different or interesting things, at least in this context.