Posted at 2017.01.08 Category : Economist
機械翻訳のニュースが出ると日本人はドラえもんの道具「ほんやくコンニャク」を引き合いに出したくなりますが、英語圏の場合はどうなるでしょうか。
Universal TranslatorとかBabel Fishなんかの例を挙げるとピンとくるようです。文化が違うとイメージしやすい例も変わる、こういった部分も翻訳の難しさです。ちなみに米国版ドラえもんでは「ほんやくコンニャク」はTranslation Gummyだそうです。どうしても日本語の音遊びの部分はなくなってしまいますね。
Machine translation: Beyond Babel
Computer translations have got strikingly better, but still need human input
IN “STAR TREK” it was a hand-held Universal Translator; in “The Hitchhiker’s Guide to the Galaxy” it was the Babel Fish popped conveniently into the ear. In science fiction, the meeting of distant civilisations generally requires some kind of device to allow them to talk. High-quality automated translation seems even more magical than other kinds of language technology because many humans struggle to speak more than one language, let alone translate from one to another.
いきなり3番目のTechnology Quarterlyの記事を紹介してしまいましたが、最初の記事は全体のまとめなので最初だけでも目を通すのがいいでしょう。映画『2001年宇宙の旅』を引き合いに出して現状の技術でできる段階を示すところから始めています。まだまだの部分があるということですね。
個別の記事ではちょっと細かい話もしているので興味がない人にはついていくのがしんどいでしょうが、言語をどのように処理するのかに関心がある人は面白く感じてもらえると思います。
FINDING A VOICE
Language: Finding a voice
Computers have got much better at translation, voice recognition and speech synthesis, says Lane Greene. But they still don’t understand the meaning of language
I’M SORRY, Dave. I’m afraid I can’t do that.” With chilling calm, HAL 9000, the on-board computer in “2001: A Space Odyssey”, refuses to open the doors to Dave Bowman, an astronaut who had ventured outside the ship. HAL’s decision to turn on his human companion reflected a wave of fear about intelligent computers.
When the film came out in 1968, computers that could have proper conversations with humans seemed nearly as far away as manned flight to Jupiter. Since then, humankind has progressed quite a lot farther with building machines that it can talk to, and that can respond with something resembling natural speech. Even so, communication remains difficult. If “2001” had been made to reflect the state of today’s language technology, the conversation might have gone something like this: “Open the pod bay doors, Hal.” “I’m sorry, Dave. I didn’t understand the question.” “Open the pod bay doors, Hal.” “I have a list of eBay results about pod doors, Dave.”
2番目の記事は音声認識。先ほどのエントリーで触れた音声認識では「予測」と「トピックの理解」が重要という部分です。
Speech recognition: I hear you
Computers have made huge strides in understanding human speech
Perhaps the most important feature of a speech-recognition system is its set of expectations about what someone is likely to say, or its “language model”. Like other training data, the language models are based on large amounts of real human speech, transcribed into text. When a speech-recognition system “hears” a stream of sound, it makes a number of guesses about what has been said, then calculates the odds that it has found the right one, based on the kinds of words, phrases and clauses it has seen earlier in the training text.
******
Advance knowledge of what kinds of things the speaker might be talking about also increases accuracy. Words like “phlebitis” and “gastrointestinal” are not common in general discourse, and uncommon words are ranked lower in the probability tables the software uses to guess what it has heard. But these words are common in medicine, so creating software trained to look out for such words considerably improves the result. This can be done by feeding the system a large number of documents written by the speaker whose voice is to be recognised; common words and phrases can be extracted to improve the system’s guesses.
音声合成の記事では実際の音声を聴き比べることができます。An advanced sampleは本物と言っても気づかない人がいそうなほどの出来栄えです。ただ短文レベルではうまく読めてもストーリーを語るレベルにはまだまだのようです。
Hasta la vista, robot voice
Machines are starting to sound more like humans
But prosody matters when someone is telling a story. Pitch, speed and volume can be used to pass quickly over things that are already known, or to build interest and tension for new information. Myriad tiny clues communicate the speaker’s attitude to his subject. The phrase “a German teacher”, with stress on the word “German”, may, in the context of a story, not be a teacher of German, but a teacher being explicitly contrasted with a teacher who happens to be French or British.
背景知識派のYutaとしては以下の記事が一番面白かったです。会話を一定期間進めるには常識と実世界の知識が不可欠だというのです。
Meaning and machine intelligence: What are you talking about?
Machines cannot conduct proper conversations with humans because they do not understand the world
以下の部分で構文解析は「自然言語処理」でできるようになっているが意味を理解する「自然言語理解」はまだまだだというのです。
How do natural-language platforms know what people want? They not only recognise the words a person uses, but break down speech for both grammar and meaning. Grammar parsing is relatively advanced; it is the domain of the well-established field of “natural-language processing”. But meaning comes under the heading of “natural-language understanding”, which is far harder.
常識というと大げさに聞こえるかもしれませんが、普段なら意識することがなく理解していることも機械で処理させるには苦労させることがあるようです。This is not drinking water.という文を例に挙げて説明してくれています。
First, parsing. Most people are not very good at analysing the syntax of sentences, but computers have become quite adept at it, even though most sentences are ambiguous in ways humans are rarely aware of. Take a sign on a public fountain that says, “This is not drinking water.” Humans understand it to mean that the water (“this”) is not a certain kind of water (“drinking water”). But a computer might just as easily parse it to say that “this” (the fountain) is not at present doing something (“drinking water”).
人間なら無生物主語が「飲む」という動作をすることはないので「これは水を飲んでいません」という解釈はありえず「これは飲み水ではありません＝これは飲めません」という意味をとっさに判断できるのですが、このような結論に至るには様々な処理を経ないといけないようです。
Shared information is also built up over the course of a conversation, which is why digital assistants can struggle with twists and turns in conversations. Tell an assistant, “I’d like to go to an Italian restaurant with my wife,” and it might suggest a restaurant. But then ask, “is it close to her office?”, and the assistant must grasp the meanings of “it” (the restaurant) and “her” (the wife), which it will find surprisingly tricky. Nuance, the language-technology firm, which provides natural-language platforms to many other companies, is working on a “concierge” that can handle this type of challenge, but it is still a prototype.
Such a concierge must also offer only restaurants that are open. Linking requests to common sense (knowing that no one wants to be sent to a closed restaurant), as well as a knowledge of the real world (knowing which restaurants are closed), is one of the most difficult challenges for language technologies.
音声認識、音声合成などの技術は進んでも、コンピュータに常識を教え、実世界の知識も教え、それらを全て結びつけて処理させるのはまだまだのようです。
Proper conversation between humans and machines can be seen as a series of linked challenges: speech recognition, speech synthesis, syntactic analysis, semantic analysis, pragmatic understanding, dialogue, common sense and real-world knowledge. Because all the technologies have to work together, the chain as a whole is only as strong as its weakest link, and the first few of these are far better developed than the last few.
The hardest part is linking them together. Scientists do not know how the human brain draws on so many different kinds of knowledge at the same time. Programming a machine to replicate that feat is very much a work in progress.
「意味を理解する」「実世界を理解する」というのが人間の特長と言っても新井教授が警鐘を鳴らしていたように、そのような全体像を理解しようとすることが人間の方にもできているのかというのが問題として残りますよね。受験英語派もTOEIC派も気になるのは「実世界を理解する」姿勢がほとんど見られない人が多いからです。だからいつまでたっても教材止まりですよね。
Universal TranslatorとかBabel Fishなんかの例を挙げるとピンとくるようです。文化が違うとイメージしやすい例も変わる、こういった部分も翻訳の難しさです。ちなみに米国版ドラえもんでは「ほんやくコンニャク」はTranslation Gummyだそうです。どうしても日本語の音遊びの部分はなくなってしまいますね。
Machine translation: Beyond Babel
Computer translations have got strikingly better, but still need human input
IN “STAR TREK” it was a hand-held Universal Translator; in “The Hitchhiker’s Guide to the Galaxy” it was the Babel Fish popped conveniently into the ear. In science fiction, the meeting of distant civilisations generally requires some kind of device to allow them to talk. High-quality automated translation seems even more magical than other kinds of language technology because many humans struggle to speak more than one language, let alone translate from one to another.
いきなり3番目のTechnology Quarterlyの記事を紹介してしまいましたが、最初の記事は全体のまとめなので最初だけでも目を通すのがいいでしょう。映画『2001年宇宙の旅』を引き合いに出して現状の技術でできる段階を示すところから始めています。まだまだの部分があるということですね。
個別の記事ではちょっと細かい話もしているので興味がない人にはついていくのがしんどいでしょうが、言語をどのように処理するのかに関心がある人は面白く感じてもらえると思います。
FINDING A VOICE
Language: Finding a voice
Computers have got much better at translation, voice recognition and speech synthesis, says Lane Greene. But they still don’t understand the meaning of language
I’M SORRY, Dave. I’m afraid I can’t do that.” With chilling calm, HAL 9000, the on-board computer in “2001: A Space Odyssey”, refuses to open the doors to Dave Bowman, an astronaut who had ventured outside the ship. HAL’s decision to turn on his human companion reflected a wave of fear about intelligent computers.
When the film came out in 1968, computers that could have proper conversations with humans seemed nearly as far away as manned flight to Jupiter. Since then, humankind has progressed quite a lot farther with building machines that it can talk to, and that can respond with something resembling natural speech. Even so, communication remains difficult. If “2001” had been made to reflect the state of today’s language technology, the conversation might have gone something like this: “Open the pod bay doors, Hal.” “I’m sorry, Dave. I didn’t understand the question.” “Open the pod bay doors, Hal.” “I have a list of eBay results about pod doors, Dave.”
2番目の記事は音声認識。先ほどのエントリーで触れた音声認識では「予測」と「トピックの理解」が重要という部分です。
Speech recognition: I hear you
Computers have made huge strides in understanding human speech
Perhaps the most important feature of a speech-recognition system is its set of expectations about what someone is likely to say, or its “language model”. Like other training data, the language models are based on large amounts of real human speech, transcribed into text. When a speech-recognition system “hears” a stream of sound, it makes a number of guesses about what has been said, then calculates the odds that it has found the right one, based on the kinds of words, phrases and clauses it has seen earlier in the training text.
******
Advance knowledge of what kinds of things the speaker might be talking about also increases accuracy. Words like “phlebitis” and “gastrointestinal” are not common in general discourse, and uncommon words are ranked lower in the probability tables the software uses to guess what it has heard. But these words are common in medicine, so creating software trained to look out for such words considerably improves the result. This can be done by feeding the system a large number of documents written by the speaker whose voice is to be recognised; common words and phrases can be extracted to improve the system’s guesses.
音声合成の記事では実際の音声を聴き比べることができます。An advanced sampleは本物と言っても気づかない人がいそうなほどの出来栄えです。ただ短文レベルではうまく読めてもストーリーを語るレベルにはまだまだのようです。
Hasta la vista, robot voice
Machines are starting to sound more like humans
But prosody matters when someone is telling a story. Pitch, speed and volume can be used to pass quickly over things that are already known, or to build interest and tension for new information. Myriad tiny clues communicate the speaker’s attitude to his subject. The phrase “a German teacher”, with stress on the word “German”, may, in the context of a story, not be a teacher of German, but a teacher being explicitly contrasted with a teacher who happens to be French or British.
背景知識派のYutaとしては以下の記事が一番面白かったです。会話を一定期間進めるには常識と実世界の知識が不可欠だというのです。
Meaning and machine intelligence: What are you talking about?
Machines cannot conduct proper conversations with humans because they do not understand the world
以下の部分で構文解析は「自然言語処理」でできるようになっているが意味を理解する「自然言語理解」はまだまだだというのです。
How do natural-language platforms know what people want? They not only recognise the words a person uses, but break down speech for both grammar and meaning. Grammar parsing is relatively advanced; it is the domain of the well-established field of “natural-language processing”. But meaning comes under the heading of “natural-language understanding”, which is far harder.
常識というと大げさに聞こえるかもしれませんが、普段なら意識することがなく理解していることも機械で処理させるには苦労させることがあるようです。This is not drinking water.という文を例に挙げて説明してくれています。
First, parsing. Most people are not very good at analysing the syntax of sentences, but computers have become quite adept at it, even though most sentences are ambiguous in ways humans are rarely aware of. Take a sign on a public fountain that says, “This is not drinking water.” Humans understand it to mean that the water (“this”) is not a certain kind of water (“drinking water”). But a computer might just as easily parse it to say that “this” (the fountain) is not at present doing something (“drinking water”).
人間なら無生物主語が「飲む」という動作をすることはないので「これは水を飲んでいません」という解釈はありえず「これは飲み水ではありません＝これは飲めません」という意味をとっさに判断できるのですが、このような結論に至るには様々な処理を経ないといけないようです。
Shared information is also built up over the course of a conversation, which is why digital assistants can struggle with twists and turns in conversations. Tell an assistant, “I’d like to go to an Italian restaurant with my wife,” and it might suggest a restaurant. But then ask, “is it close to her office?”, and the assistant must grasp the meanings of “it” (the restaurant) and “her” (the wife), which it will find surprisingly tricky. Nuance, the language-technology firm, which provides natural-language platforms to many other companies, is working on a “concierge” that can handle this type of challenge, but it is still a prototype.
Such a concierge must also offer only restaurants that are open. Linking requests to common sense (knowing that no one wants to be sent to a closed restaurant), as well as a knowledge of the real world (knowing which restaurants are closed), is one of the most difficult challenges for language technologies.
音声認識、音声合成などの技術は進んでも、コンピュータに常識を教え、実世界の知識も教え、それらを全て結びつけて処理させるのはまだまだのようです。
Proper conversation between humans and machines can be seen as a series of linked challenges: speech recognition, speech synthesis, syntactic analysis, semantic analysis, pragmatic understanding, dialogue, common sense and real-world knowledge. Because all the technologies have to work together, the chain as a whole is only as strong as its weakest link, and the first few of these are far better developed than the last few.
The hardest part is linking them together. Scientists do not know how the human brain draws on so many different kinds of knowledge at the same time. Programming a machine to replicate that feat is very much a work in progress.
「意味を理解する」「実世界を理解する」というのが人間の特長と言っても新井教授が警鐘を鳴らしていたように、そのような全体像を理解しようとすることが人間の方にもできているのかというのが問題として残りますよね。受験英語派もTOEIC派も気になるのは「実世界を理解する」姿勢がほとんど見られない人が多いからです。だからいつまでたっても教材止まりですよね。
