I recently have begun giving serious thought to what command chaining might look like in Ubiquity and the various considerations which must be made to make it happen. The “command chaining,” or “piping,” described here always involves (at least) two verbs acting sequentially on a passed target—that is, the first command performs some action or lookup and the second command acts on the first command’s output.

A few days ago I penned some initial technical considerations regarding command chaining. In this post I’ll be point out some linguistic considerations involved in supporting a natural syntax for chaining.

Simple syntaxes: sequential vs embedding strategies

When it comes to creating a natural language interface, there’s always a decision to make between requiring a certain kind of input, or working a little harder to understand the user’s natural input. From an implementation point of view, adopting certain programmatic conventions is of course simpler and to this end, there have been a couple different “unnatural” command chaining syntaxes suggested. While these both go against Ubiquity’s basic tenet of natural syntax — that is, to not introduce rules which contradict the user’s natural language — which gives Ubiquity its strengths of usability and memorability, I’ll entertain them here as they illustrate two different structural relationships that we will want to consider.

The first suggestion is to adopt the shell pipe (|), which would lead to input such as

1

translate hello to Spanish | email to Jono

While this itself is pretty unnatural unless you speak shell, note that this syntax is similar to the more natural “, and” syntax, yielding translate hello to Spanish, and email to Jono, which we will consider below. I’ll refer to this strategy as the sequential strategy.

Another very interesting proposal by Alex Fritze is to embed each subordinate computation into an argument position, marked by parentheses. This could also be parsed relatively straightforwardly by writing a noun type which first checks for parentheses and then runs the content of the argument through another ParseQuery.

2

email (translate hello to Spanish) to Jono

I’ll refer to this pattern as the embedding strategy.

Sequential and embedding strategies in natural language

What’s interesting about the two proposals above is that both strategies are seen in natural language. The sequential strategy could correspond to the following linguistic phenomena:

1. coordination: a non-hierarchical joining of two or more clauses, often marked by a conjunction. Here’s an example from English:
   • “[I made a sandwich] and [you will eat it]” where [] represent clause boundaries. Here, “and” is the conjunction.
2. serial verb and converb constructions: a joining of multiple verbs or verb phrases within a single clause, with shared subject and tense/aspect values, with no particular conjugation or delimiter between them. Such constructions are common in many African and east Asian languages. Here are two examples:¹
   

• A converbal construction in Japanese:
  

  3 4 5

  僕は サンドイッチを 作って 食べる boku-wa sandiʔchi-o tsuku-ʔte tabe-ru I-TOP sandwich-ACC make-CON eat

  
  “I (will) make a sandwich and eat [it].” (Here, `TOP` = topic, `ACC` = accusative, `CON` = converbal ending)[^3]
• A serial verb construction in Mandarin Chinese:
  

  6 7 8

  我 作 三明治 吃 wǒ zùo sānmíngzhì chī I make sandwich eat

  
  “I (will) make a sandwich and eat [it]” or “I (will) make a sandwich [in order to] eat [it].”

Note that in both the converb and serial verb construction, the second verb (eat) takes shares its object (sandwich) with the first verb and there is no need for a pronoun such as “it” to introduce that argument as it is with coordination, above.²

The embedding strategy is observed in natural language as well, in the form of the following phenomena:

1. embedded clauses: a sentence is itself the argument of another verb. Example:
   

• “John says [he likes sandwiches].”

Embedded clauses, however, clearly have no relation to command chaining and does not require our attention. 2. relative clauses: a partial sentence³ is attached to a noun in order to describe it or distinguish it from other possible referents. Example:

• “You ate the sandwich that I made” where “sandwich” is called the “head” of the relative clause, and “I made” is what I here call the “partial sentence” (see footnote). The “relative clause” is used here to distinguish “the sandwich that I made” from other sandwiches.

The natural syntax of chaining

So which strategy is used in complex natural language commands: the sequential strategy or the embedding strategy? Both the sequential strategy and embedding strategy can be involved with commands:

9
10

[Make a sandwich] and [eat it]!
Eat (the sandwich that I made)!

These two commands do not mean the same thing, though, and only (9) is the kind of command we would want to give Ubiquity. The problem with relative clauses, as in (10), is that it presupposes the existence of the sandwich in the context. If we both know you just made a sandwich, saying (10) is perfectly appropriate, but out of the blue it doesn’t make sense. For this reason, only the sequential strategy is used in the natural syntax of chaining.

Parsing the sequential strategy

In natural language, unlike the initial simple proposals laid out above, there is often no clear delimiter marking the boundary between the two parts in a sequential relation (e.g. examples (3) and (6) above, particularly given that neither Japanese and Chinese normally break words with spaces). How would we parse a sequential string of commands?

Let’s assume for our purposes here that we can identify find all verbs within the input string.⁴ Parsing a sequential strategy string is not particularly difficult if we can also assume that the verb in any particular language is either always verb-initial or always verb-final. Let’s look at both cases:

• Always verb-initial: Mandarin Chinese:
  

11
12
13

翻譯       hello 到  西班牙語   送    給  Juanito
fānyì     hello dào xībānyáyǔ sòng gěi Juanito
translate hello to  Spanish   send to  Juanito

“Translate hello to Spanish [and] send [it] to Juanito” 1. find every possible verb:
翻譯hello到西班牙語送給Juanito 2. as every verb marks the beginning of a sentence, we now have our two commands: “翻譯hello到西班牙語” (translate hello to Spanish) and “送給Juanito” (send to Juanito). * Always verb-final: Japanese

14
15
16

helloを スペイン語に 訳して Juanitoに 送って
hello-o supeingo-ni yakus-ite Juanito-ni oku-ʔte
hello-ACC Spanish-DAT translate-CON Juanito-DAT send-CON

“Translate hello to Spanish [and] send [it] to Juanito” 1. find every possible verb:
helloをスペイン語に訳してJuanitoに送って 2. as every verb marks the end of a sentence, we now have our two commands: “helloをスペイン語に訳して” (translate hello to Spanish) and “Juanitoに送って” (send to Juanito).

For languages where there is a clear conjunction between the two commands, such as English “and”, we can also use that conjunction as a delimiter as well. We then simply execute the first command and then execute the second with the first command’s output in its interpolation context. This way the output of the first command will be picked up both by an overt pronoun such as “it” in the second command and without it, such as in the Chinese and Japanese examples above.⁵

The only potential problem with this approach is in the case of languages where some commands are verb-initial while others are verb-final. I note that such languages do exist in a previous blog post, Where’s The Verb. In these languages, commands can be expressed by more than one verb form (such as infinitive, imperative, subjunctive, etc.) and some of those verb forms are sentence-initial while others are sentence-final. Here’s one such example from German:

“search hello with google” (German) 1. Infinitive: hello mit google suchen 2. Imperative: suche hello mit google

Here the verb for “search” is “suchen” (infinitive) or “suche” (imperative). I know that this same type of phenomena occurs in other Germanic languages such as Dutch with infinitive and imperative and also other languages such as Modern Greek with infinitive and subjunctive forms. If you are a speaker of one of these lanuages (German, Dutch, Greek, etc.) I would love to know whether you can chain verb-final and verb-initial commands together.

Conclusion

In this blog post I examined command chaining in natural language, focusing on data from English, Mandarin Chinese, and Japanese, which exhibit three linguistically different approaches to chaining. What we found is that the sequential strategy—that of listing the commands one by one, in order of execution—is what is used in natural languages, rather than any sort of embedding. This fact, combined with the fact that our parser can recognize every available verb, offers a simple approach to doing a naive parse of natural command chains in most languages, even without explicit delimiters.

In a final installation of this series on “exploring command chaining,” I hope to consider how the Ubiquity interface itself could present command chains and aid in its input.

Related posts:

1. Exploring Command Chaining in Ubiquity: Part 1
2. Command Chaining with Oni?
3. Ubiquity in Italian

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Mozilla Labs の実験的プロジェクトのひとつ、 Ubiquity の最新版、バージョン 0.5 を昨日リリースしました。 (Mozilla Labs 正式発表 [英文])

Ubiquity はウェブをより有益に、より使いやすくするために自然言語で Firefox を操作するインターフェースを提供します。ウェブ上のオープン API と機能が増えて行く一方でどのようなインターフェースが必要であるのか。その答えを追求した結果、テキスト入力の正確さとスピードと自然言語の心地よさを合わせたインターフェースができあがりました。例えば「麹町を地図で表示」、「これを (誰々) へメール」などを自分の言葉で入力してブラウザを操作することができます。新しいコマンド (動詞) も簡単に JavaScript で書けるので、拡張性も非常に高いプラットフォームです。

ユーザにとって「自然な構文」 (“natural syntax” [英文]) という目標の下、数ヶ月の研究の結果、Ubiquity 0.5 では複数の言語の異なる構文に対応できるパーサを実装しました。Ubiquity 内蔵のコマンドもローカライズ可能になり、0.5 ではすべての内蔵コマンドの日本語、デンマーク語とポルトガル語版が搭載されています。

リリース直前に Ubiquity の日本語紹介ビデオを作成しましたので、どうぞご覧ください。日本語モードでの使用方法も説明されています。

Ubiquity 0.5 日本語紹介ビデオ from mitcho on Vimeo.

日本語サポートが入った Ubiquity 0.5 を是非ご使用ください。このインターフェースをより多くのユーザが「自然に」使えるよう、これからも開発を続けていきたいと思います。

Related posts:

1. Contribute to Ubiquity! No Coding Required!
2. Talking Ubiquity in Japan: 拡張機能勉強会にて発表
3. Ubiquity presentation at Tokyo 2.0

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追伸： ７月３日現在、 Ubiquity 0.5 のリリースを遅らせる方向になったので、残念ながら今日はリリースされません。是非リリース後インストールしてみてください。

Ubiquity 0.5 日本語紹介ビデオ from mitcho on Vimeo.

As Ubiquity 0.5 will be released soon (Thursday morning in Mountain View), I decided it was a good time to put together a screencast in Japanese demoing the use of the new Japanese parser and commands.

Related posts:

1. Changes to Ubiquity Parser 2 and the Playpen
2. Foxkeh demos Ubiquity Parser: The Next Generation
3. A Demonstration of Ubiquity Parser 2

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This past Monday I presented at Tokyo 2.0, Japan’s largest bilingual web/tech community. I presented as part of a session on The Web and Language, which I also helped organize. Other presenters included Junji Tomita from goo Labs, Shinjyou Sunao of Knowledge Creation, developers of the Voice Delivery System API, and Chris Salzberg of Global Voices Online on community translation.

I just put together a video of my Ubiquity presentation, mixing the audio recorded live at the presentation together with a screencast of my slides for better visibility. The presentation is 10 minutes long and is bilingual, English and Japanese.

Ubiquity: Command the Web with Language 言葉で操作する Web from mitcho on Vimeo.

The event also coincided with Dave McClure’s Geeks on a Plane Asia tour, attracting even more interest to the event. In the end it was the largest Tokyo 2.0 event ever.

As I leave Tokyo next month, I’ll be sad to not be able to continue to be a part of Tokyo 2.0. I’ve met a lot of fascinating people and learned a lot at the monthly events. I’ll definitely make sure to schedule them in in my future travels back to Japan and I highly recommend any of you who travel to Tokyo do so as well.

Related posts:

1. Changes to Ubiquity Parser 2 and the Playpen
2. Foxkeh demos Ubiquity Parser: The Next Generation
3. Notes from BarCamp Tokyo 2009

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昨日は文部科学省の先端ITスペシャリスト育成プログラムの学生の為に講義をしました。ちょっと長めの時間だったので、二つのトークに分けてみました。第一部は「オープン」と言うアイデア、特にオープンなデザイン過程の利点について。第二部は未来のインターフェースと国際化とUbiquityの紹介でした。スライドをここにslideshareにあげておきました。

Design processes in the open-source era オープンソース時代のデザインプロセス

Ubiquity: Interfaces and Internationalization インターフェースと国際化

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1. Notes from BarCamp Tokyo 2009
2. This week on Ubiquity Parser: The Next Generation
3. Report from Mozilla Party JP 10!

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Every day on the way to work I walk by a fine establishment known as Yoshinoya (吉野家), Japan’s largest gyudon (牛丼) chain restaurant. For those of you whose lives have yet to be graced by gyudon, it’s a bowl of rice topped with beef and onions stewed in a sweet-savory soy-based sauce. Loving gyudon and being a cheapskate, I naturally noticed the recent 50 yen off gyudon promotion at Yoshinoya. The above photo is a photo of part of that sign.

Part of this sign, though, made me think about our new Ubiquity parser. In particular, it was the attachment ambiguity in the end date of the promotion. The text in the photo above literally is “April 15th (Wed.) 8PM until”. (Note that Japanese is a strongly head-final language, and that the “until” is a postposition.) There are two possible readings for this expression, as illustrated by the two composition trees below.

The first tree, on the left, represents the reading “until (April 15th 8PM)”, while the second represents two arguments: “on April 15th” and “until 8PM”. In other words, in the first reading, the promotion begins at some earlier date and extends until April 15th at 8PM while, in the second reading, the promotion is one day only, on April 15th, until 8pm. Such syntactic ambiguities are called “attachment ambiguities” in linguistics as it is an ambiguity of where different arguments “attach” in a tree representation.

This attachment ambiguity was possible because there was no clear marker on “April 15th,” which may have disambiguated it as “on April 15th”. In fact, in many languages this time position argument comes with no case marker or preposition, or it’s optional, making parsing for them difficult. If such a sentence is entered with spaces, the Ubiquity Parser: The Next Generation would try a parse where “8PM” is the “until” or goal argument and “April 15th” is an object argument, but it will only check its noun type, not put it in the correct semantic role (position). Perhaps this is something to think about in the future.

These types of situations will surely come up as we continue work on the Ubiquity parser, making it essential to look at different languages. Are there certain kinds of arguments in your language that do not have any word-external markers such as case or prepositions/postpositions?

Related posts:

1. User-Aided Disambiguation: a demo
2. Ubiquity in Firefox: Focus on Japanese
3. Talking Ubiquity in Japan: 拡張機能勉強会にて発表

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Last weekend my family (including my aunt Mikako and Bailey) took a short trip to Yugawara (湯河原) at the base of the Izu peninsula. Last weekend was possibly the peak of the cherry blossoms this year, making it a very picturesque trip. It’s quite rare for the four of us to all be in the same place at the same time, so these photos are definite keepers:

One of my personal highlights was going down a slide at Azumayama Park in Ninomiya right through a grove of cherry trees in full bloom—it was so beautiful that I had to go back down it again and take a video! Unfortunately the Flash video encoding (or my camera) doesn’t do it justice, but I hope you can fill in the gaps with your imagination.

Cherry blossom slide - 桜のすべりだい（二宮吾妻山公園） from mitcho on Vimeo.

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1. Weekend in Osaka
2. 北京 Part 3: The Great Wall of China! and noodles
3. 新年快樂! Chinese New Year with Andy

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昨日第11回Mozilla拡張機能勉強会で Ubiquity の国際化と次世代パーサについて発表してきました。色々鋭いコメントをいただき、僕も良い勉強になりました。^^ スライドの方をslideshareに載せたので、是非参考にまた見てみてください。ライトニングトークでも Ubiquity の話で盛り上がったので、そのLTの内容で特に面白いと僕が思ったものを下に英語でちょっとまとめてみます。

1. mar of Japanese SuMo fame (not that sumo) presented on his foray into the development of an improved Japanese parser based on Jono’s. One interesting approach his parser took was to split up the input on delimiters like commas and parse each “clause” and then combining the arguments for one execution. This allows certain types of fronting constructions. For example:

   ...を   送って、 dynamisに
   ...-NOM send, dynamis-to

   “To dynamis, send …”

   This type of input, aside from being pretty natural in Japanese, has the advantage of offering the parser an unambiguous argument parse within each clause, cutting down on the possible ambiguities.

   mar’s discussion, however, also naturally touched on the limitations of the current NLParser implementation, making localization of individual commands and the suggestion of verbs quite difficult.

2. Hitoshi SASAKI of the Sasaki Lab at Takushoku University discussed some possible applications of Ubiquity in an educational context. In particular he demoed a `hiragana` command which takes some sentence in kanji and rewrites it in hiragana, the Japanese phonetic alphabet. What’s more, the command lets you specify the appropriate grade level for the substitution, making it appropriate for elementary school kids and non-native speakers alike. Sasaki thought the ability to access this kind of functionality right from the content page was of great benefit to this application.

Thanks to dynamis for supporting my Japanese presentation and making this happen! ^^

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1. This week on Ubiquity Parser: The Next Generation
2. Ubiquity in Firefox: Focus on Japanese
3. Ubiquity Parser: The Next Generation Demo

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Last week I released a proof-of-concept demo of the next generation Ubiquity parser design and it was also the focus of discussion in our weekly internationalization meeting.¹ Christian Sonne even wrote a Danish plugin for it during the meeting—a testament to the pluggability and of the new parser design.

In addition, at the Ubiquity weekly meeting, pushing this new parser into Ubiquity proper was identified as a key goal of Ubiquity 0.2, making frequent iteration and debate over this parser essential.

To that end, I’ll highlight some of the changes made to the parser demo codebase in the past week:

• left-branching support and a Japanese parser
• basic French parser
• a timer display
• Danish parser by Christian Sonne
• synonyms: as an example, you can now use “purchase” or “buy,” both of which point to the same verb.
• verb name localizations: you no longer need to use the English verb names with different languages. (Currently only Japanese has any verb localizations.)
• a number of optimizations and corrections

I encourage you to check out the demo again or check out the source on BitBucket.

➔ Ubiquity Parser: The Next Generation demo

I’d love to get comments, patches, or additional parsers! Thanks! ^^

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1. Ubiquity Parser: The Next Generation Demo
2. Ubiquity in Portuguese
3. Localizing Ubiquity: an open letter to linguists

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The inspiration for this idea came from Aza’s blog post “Solving the ‘it’ problem” which advocates for this type of quick feedback to the user in cases of ambiguity. Such a method would help both the user better understand what is being interpreted by the system, as well as offer an opportunity for the user to correct improper parses. I just tried mocking up such an input box using jQuery.

➔ Try the User-Aided Disambiguation Demo

If you have any bugfixes to submit or want to play around with your own copy, the demo code is up on BitBucket. ^^ Let me know what you think!

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1. How natural should a natural interface be?
2. Ubiquity in Firefox: Focus on Japanese
3. Friendlier command feed subscription

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今後多様な言語に対応したFirefox内のUbiquityを検討していきますが、その中でも今日は日本語をとりあげます。後日日本語で同じ内容を投稿するつもりです。^^ 日本語でのコメントも大歓迎です！

What commands look like in Japanese

Japanese is not only just a verb-final language but it is strongly head-final, meaning it has postpositions instead of prepositions, direct objects come before verbs, and adjectives precede nouns. In terms of how it identifies its arguments, every argument has a postposition/case marker (called a particle in the Japanese literature) which marks its role in the sentence.

A couple common particles we’ll look at in this example include -を (-o) which marks the direct object (accusative case, you might say) and -に (-ni) which acts like English “to” (dative case). The example sentence we’ll look at today is:

(Note: ʔ is a glottal stop. ACC=accusative, DAT=dative, and IMP=imperative form.)

That final ください is often dropped in very casual speech and, as it adds no new information, we’ll assume today that the user will not enter it. Finally, Japanese doesn’t use spaces in their orthography, so the actual input would be “ケーキをブレアに送って”.

Mockup 1: Particle identification

One of the major hurdles in working with Japanese is that there are no spaces between the words. The natural first step is to split the sentence up into words, but this is a very difficult problem in NLP which big name research groups actively work on.

Fortunately, however, in “Solving the ‘It’ Problem” Aza suggests that, when we encounter ambiguity in our input, we can go ask the user. Great minds think alike, and computer scientist Jean E. Sammet suggested the same idea way back in 1953:

Using English [or any other natural language] definitely involves the requirement for the computer (or more accurately its programming system) to query the user about any possible ambiguity.

Parsing a sentence into words, in the limited context of Ubiquity, is really about identifying the particles which mark the end of each argument. Here’s a mockup of an application of the Sammet-Raskin Method to this problem:

Pros: This completely takes care of the word-breaking problem, with minimal arbitration from the user. The parser knows exactly what arguments it’s dealing with and the visual feedback means the user won’t be surprised by the parse.

Cons: Most of the particles/postpositions we’d have to deal with are a single character, so they may show up pretty often within words, in which case it would be quite annoying to have to press escape after each one.

An even smarter system, when wanting to mark a character as a particle, would first check to see that the argument (before the particle) is a valid argument type for that particle. If the check fails, it doesn’t have to bother with suggesting that character as a particle. This may cut down on the false positives.

Smart suggestions: what works, what doesn’t

One of the key suggestions in Aza’s mockups include a way to choose the prepositions while entering your arguments, based on the current verb.

For example, here, the translate command accepts a direct object, a to-object, and a from-object, so little to and from markers magically show up on the right side, making the appropriate prepositions (and by extension the appropriate arguments) discoverable. I think this line of thinking is a really good one, at least for English.

In a verb-final language, however, you enter the arguments first and then the verb, making this strategy of suggesting appropriate arguments impossible. Note that in the user-contributed spreadsheet of how languages identify their arguments we see that about a quarter of the languages we looked at are verb-final—that is, with Subject-Object-Verb canonical word order.

Instead of seeing this as a disadvantage, however, let’s see what verb-final order allows us to do.

Mockup 2: A different kind of suggestion

Not all verbs allow for every different kind of particle. For example, it doesn’t make sense to have a -に (-ni, “to” or dative) argument for a verb like 検索して (kensaku-shite, “search for”). In English we used this to suggest different types of arguments given a specific verb. In a verb-final language, we could do this backwards.

Pros: This makes verbs highly discoverable, given a certain argument structure. For example, if you enter a few arguments, like a direct object, a “to” argument, and a “from” argument, it’ll suggest verbs that will do something to an object from somewhere to somewhere else. This way, you can easily try out verbs you didn’t even know existed. It’ll only give you verbs appropriate for your arguments, reducing the chance of writing a an infelicitous command.

Cons: Without knowing what kinds of actions are available, it may be difficult to know what kinds of arguments to enter in the first place. If you have a specific verb or service you want to use it may be counterintuitive or downright tricky to start by guessing the right set of arguments.

In addition, from a technical point of view, this requires much of the prediction algorithms in English Ubiquity to run backwards. Ideally, there would be a closed (predetermined) class of particles and a predefined set of noun types. Verbs would not be able to define their own modifiers and noun classes as easily or freely as they can now.

Conclusion

The properties and challenges of Japanese grammar require that we not try to outright copy the English behavior but to think about what really makes sense in that language and that may be an important lesson as we move toward designing a localizable Ubiquity. Please post your questions and criticisms of this design or post your own mockups!

Related posts:

1. Three ways to argue over arguments
2. Contribute: how your language identifies its arguments
3. How natural should a natural interface be?

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When we execute a command in Ubiquity, in very simple terms, we’re hoping to do something (a verb) to some arguments (the nouns). Every sentence in every language uses some method to encode which arguments correspond to which roles of the verb. Here are a couple examples:

1
2

He sees Mary.
彼が Maryを 見る。 (Kare-ga Mary-o miru.)

As speakers of English, you can read sentence (1) above and know exactly who is doing the seeing and who is being seen and speakers of Japanese can get the same information from (2). How do different languages code for arguments in different roles? There are, broadly speaking, three different ways:

We’ll take a brief look today at these three different strategies, all of which a localizeable natural language interface will surely encounter.

Word order

In many languages, the position of the arguments relative to one another and to the verb determine the roles which each argument will play. Mandarin Chinese is a good example of such a language:

3
4

他 喜欢 Mary (Ta xihuan Mary)
Mary 喜欢 他 (Mary xihuan ta)

Here, sentence (3) says “he likes Mary” while sentence (4) says “Mary likes him”. Simply reversing the positions of “he/him” and “Mary” we’re able to flip the roles that they fill in the sentence: that of the person who does the liking and the person who is being liked. Now take a look at sentence (5) which means “John says ‘hello’ to Mary.”

5

John 告诉 Mary "你 好" (John gaosu Mary "ni hao")

We note here that, while in English we used a different strategy of marking one argument (we marked the “hello” argument with “to”), Chinese doesn’t mark either of the arguments. There is, however, a clearly defined order to the arguments, which you might encode this way:

say [who you're speaking to] [what you're saying]

If you swap the order of the two objects in this sentence, it becomes ungrammatical. (Note: the asterisk * here means the sentence is ungrammatical.)

5

* John 告诉 "你 好" Mary (John gaosu "ni hao" Mary)

Here, the word order dictates that “你好” must be “who you’re speaking to” and “Mary” must be “what you’re saying,” but that doesn’t make sense, so the sentence is ungrammatical.

Marking the arguments

Another possible strategy is to mark each argument (or some of the arguments) so that each argument’s role is clear. In many languages this is done with case marking. Take for example this Ancient Greek sentence with its English gloss on line (6). Here, NOM refers to nominative case and ACC refers to accusative case.¹

5
6

ho  didaskal-os  paideuei to  paidi-on  (SVO)
the teacher -NOM teaches  the boy  -ACC

This sentence means “the teacher instructs the boy.” While sentence (5) is in Subject-Verb-Object order, any of the six possible orderings of {subject, verb, object} are also grammatical and mean the same thing:²

7
8
9
10
11

ho didaskalos to paidion paideuei (SOV)
paideuei ho didaskalos to paidion (VSO)
paideuei to paidion ho didaskalos (VOS)
to paidion ho didaskalos paideuei (OSV)
to paidion paideuei ho didaskalos (OVS)

Many languages also use adpositions (prepositions and/or postpositions) to further clarify the role of an argument in addition to case (like English does) or in lieu of case marking altogether. The idea is the same, though: you want to clarify the roles of the arguments so you morphologically mark the arguments with their roles.

Marking the verb

Many languages mark the verb with some information about the argument in a certain role, so that we can properly identify the argument’s roles. This kind of phenomenon is called agreement.

The most common type of verbal agreement is subject agreement, where the verb is marked by a specific form depending on some features of the subject. Anyone who’s taken French 101 will recognize this verb conjugation paradigm:

With this paradigm, if you hear or see “suis” in a French sentence, you immediately know that “je” (I) must be the subject and if you see “sommes,” “nous” (we) is the subject, etc. Standard Average European languages tend to exhibit this sort of subject-verb agreement.

Features of the subject position aren’t the only thing that can be marked on the verb, though. Hungarian, for example, has a type of object agreement. Specifically, the verb marks whether the object is definite or not (in linguistics lingo, “the verb agrees with the object’s definiteness feature”).

12
13
14
15

John lát  egy almát.
John sees an  apple
John látja az  almát.
John sees  the apple

Notice that in sentence (12) (glossed in (13)) the verb for “see” is realized as “lát,” while in (14) it’s “látja.” A speaker can use that agreement to see whether the object is definite or not and thus limit the possible object arguments out of all the nouns in the sentence.

All of the above

Most languages do not use only one of these strategies, but a combination of them. English is a very good example. In a sentence like (12) below the main coding of grammatical roles seems to be word order alone. By reversing the word order into (13), we can effectively swap the argument’s roles.

12
13

John likes Mary.
Mary likes John.

However, this doesn’t work with pronominal arguments. Swapping the arguments in (14) yields (15) which is ungrammatical due to the case marking on the pronouns.

14
15

He likes her.
* Her likes he.

In addition, the verb in English must agree with the subject’s number (singular or plural):

16
17
18

John likes them.
* They likes John.
They like John.

In this way, English exhibits all three strategies: word order, case marking, and agreement, although often only word order is actively used to disambiguate the roles of arguments.

Question: What strategies are used by your language to mark the roles of different arguments?

Related posts:

1. 回収 vs. 収集 and Better Word Meanings Through Usage
2. Testing Google’s Language Detection
3. Gaba, Shame On You

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photo credit: kazamatsuri

My friends and I use a site called warfish.net which lets you set up games with your friends and play sans Flash. You don’t need to play in real time, either… warfish will email you when it’s your turn, making it a great way to play with friends halfway around the world. The site is invite-only, but you can request an invite from me here.

About a week ago I tried playing from my iPhone while on the train and it worked remarkably well. The addition of a proper <meta name='viewport'> tag so I don’t have to zoom in with every reload would be even better, but I really can’t complain. This weekend I was playing on the way to and during breaks at my spacetime workshop as well.

Here’s a quick video I put together to show how it’s done on the iPhone:

Hope to play with you soon!

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• 回収 may take things away from others when collecting while 収集 does not have that implication.
• Things that you 回収 may have been previously distributed by the actor themself while 収集 does not have that implication.¹

Not content with armchair theorizing, however, I decided to take advantage of one of the largest corpora in the world: Google.² To test my hypothesis, I chose two “objects of collection”, one you can take away (and often is distributed first) and one you can’t take away: アンケート (ankēto “survey,” from the French enquête) and 意見 (iken “opinion”). I then took the four resulting collocations³ on Google in quotes (“•”) and recorded how many hits there were.

A better way to organize this data is as follows:

This data clearly supports the hypothesis I laid out above: アンケート, which can be taken away from people and is often distributed first, occurs much more likely with 回収 than 収集. 意見, on the other hand, which crucially cannot be taken away when collected, occurs much more likely with 収集 than 回収.

While this one example doesn’t prove anything in and of itself, it does help clarify with data a nuance between two near synonyms. While my hypothesis was borne out here, native speaker intuitions on word nuances and distinctions can be unreliable.⁴ This type of quick test can be very helpful for language learners and instructors alike.

Languages very often have words which vary in very subtle ways. Just this Tuesday I went to a Tokyo Language Exchange Meetup, a great meetup which brought together various language learners and enthusiasts. A hot topic that night was words with very similar meanings—near synonyms. A few English learners were lamenting sets of words like {see, view, watch} and how difficult they are to learn. I myself have had the same experience studying Mandarin.

I noted that these difficulties in offering contrasting definitions often are due to the fact that word meanings are not just “what the word points to” but also the implication of “what it relates to”.⁵ For example, “unborn baby” and “fetus” may point to the same thing, but are used in different contexts, in contrast to different other terms, for differing effect. Similarly “Death Tax” and “Estate Tax.” “Kneel” and “genuflect.”⁶

The concept of word meanings being “what it points to” and “what it relates to” also helps explain why certain words are difficult to translate. Fillmore uses the Japanese example of ぬるい (nurui) which is the de facto translation of “lukewarm.” However, some Japanese speakers will only use ぬるい in contrast with “hot,” i.e., hot tea can become ぬるい over time but ice water does not become ぬるい. In contrast, English “lukewarm” can be used to describe things that are initially or prototypically hot or cold. “What the words point to” in this case is the same but “what it relates to” or, here, “what it contrasts with” is different, making it an imperfect (though very close) translation.

Every language has near synonyms which vary slightly in nuance but this nuance or “feeling” is borne out objectively in data. Looking at what words certain terms relate to in real usage is often the key to getting a richer understanding of vocabulary.

Related posts:

1. Testing Google’s Language Detection
2. Taipei find: a dictionary of Chinese-Japanese false cognates
3. The Most Beautiful Word

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I’ve written before about Mailplane, a high-quality Gmail client with some great Mac-specific features. I’ve been happy to be associated with the project as its Japanese localizer. I recently completed the localization for the upcoming version 2.0. As a result, I’ve received twenty free licenses for Mailplane 2.0 from the developer, Ruben Bakker. Email me if you’re interested in one, and keep your eyes peeled for the 2.0 gold release.

前にもここで話題にしたことはあるが、今日は Mailplane の新バージョンを発表しよう。 Mailplane は Mac 的な機能満載の Gmail クライアントで、Gmail 2.0 対応の最新バージョン (2.0) が近々リリースされる。 自分は Mailplane の日本語版担当なので開発者のルーベン・バッカーさんから Mailplane 2.0 の無料ライセンズを20件頂きました。欲しい方はこちらにメールしてください。

尚、日本ではもうすぐ Mailplane が MacFan で紹介されるとのこと。楽しみ！最後に、日本語版で問題があると思ったら、勝手に書き上げる前に直接教えてね。^^ お願いします。m(__)m

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A mansion? By Japanese standards, yes.¹ A palace? Um… not quite. Take a look—here are some pictures from my move-in.

The apartment has three bedrooms and shared small kitchen/dining room, bath, and toilet. I live here with two other Japanese salary-men who are older than I. We all live pretty busy lives and don’t run into each other much. And, believe it or not, I found it on Tokyo Craigslist.

While it may not resemble a “palace mansion,” it definitely has its perks. For starters, it’s a fifteen-twenty minute walk from my work, which is a great plus in a land of long train commutes. It’s in a quiet, down-to-earth neighborhood—right next to the Children’s Center and a block or two from a old-style shopping district (商店街 shōtengai)—while also one train station away from Shinjuku station, which is a major transit hub.² There’s a fabulous (and apparently slightly famous) ramen a couple doors down. And finally, while small, it’s definitely one of the cheapest places I saw.

But let’s back up a moment and let you in on how I got here.

I moved from Taiwan to Japan at the end of June after the end of my Fulbright. ³ While doing the regular job search⁴ I registered with a search firm with a focus on bilinguals, Robert Walters. I had a great experience with them and ultimately accepted a position introduced to me by them. I started on August first as Online Game Programmer at Gameloft.⁵

I was weary of blogging about my job search at all during it, but I hope to break the radio silence and start blogging again about my new life here in Tokyo. ^^

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1. 北京 Part 2: Summer Palace, bargaining, The Tree, and fried apple pie
2. Co-schooling in Dongshan
3. Pinker wins, this time

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Purchased yesterday in Taipei at NET, the wannabe GAP of Taiwan.

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I’ve been a fan of the SNL Digital Shorts since Lazy Sunday, but this is absolutely something else. It’s a brilliant piece of cross-cultural parody. Many on the associated Hulu page had some questions, however, so I decided to write up a little explanation of what’s actually going on in this short, and why I love it so.¹

The Digital Short begins with the Japanese version of the intro sequence, including a shrine, a 700 series bullet train, and the Scranton city sign now showing Amagasaki (尼崎市), a similarly industrial city near Osaka. Dwight shredding paper with Japanese text and Jim eating noodles are nice touches. All the names, in case you were wondering, are possible Japanese names (modulo Jim’s actor’s name being in katakana, and thus exclusively foreign). After Michael Scott with extra black hair readjusts his lucky cat (manekineko, 招き猫), we get to the brilliant title card.

As Japanese lacks definite and indefinite articles, the word “The” is replaced with 「その」 sono, the demonstrative ‘that’, making the title literally “That Office.” Lacking a straightforward replacement for “The,” however, I feel that this is a very cute artifact of overly-faithful translation.

The short itself runs through the Japanese versions of a few key scenes from the first episode of The Office. In the first, Pam is answering the phone and Michael interrupts her in his signature way, repeating her name (or, the Japanese equivalent of “Pam”, pamu パム) and then dropping -san, a personal name suffix—the equivalent of Mister or Miss—and smiling into the camera, content with his own cleverness. Pam says something indiscernible to Michael, referring to him as Tanaka-san (the “Mr. Smith” of Japan—even though his name plate accurately said “Michael Scott” マイケル・スコット), to which Michael mumbles 「そういうことです」, a phrase meaning “and that’s that,” or “and that is the case.” My guess is that this was the attempted translation of “that’s what she said.”

Michael then goes on to say 「日本で一番面白いボスです」 (nihon-de ichiban omoshiroi bosu desu, ‘[I am] the most interesting (=funniest) boss in Japan’). Steve Carell’s snicker halfway through that line, in response to his trying really hard at producing it, is very cute. The mug itself says 「世界中で一番面白い社長」(“world’s funniest company president”). This reminds me of my dad when he speaks Japanese, in the best way possible. ^^

Next is of course the jello scene. Dwight picks up the phone with the phone-appropriate 「もしもし」 (moshimoshi) and Jim asks where the stapler is. Dwight yells back 「バカ！」 (baka, ‘stupid!’) and Pam laughs, though in the stereotypical Japanese female’s high pitch manner, appropriately covering her mouth (though Pam also actually does this in the original). Michael walks in and they all apologize, 「ごめんなさい」 gomennasai. Although the bowing is a bit excessive in a classical SNL parody way, the traditionally hierarchical status quo of Japanese offices is very succinctly reflected here.

The next scene is also from the first episode of The Office, where Michael introduces himself, 「僕は君たちのリージョナル・マネージャ」 (boku-wa kimitachi-no regional manager, ‘I am your Regional Manager’). Dwight states that he is the “Assistant Regional Manager” (アシスタント・リージョナル・マネージャ) and then is corrected, 「リージョナル・マネージャのアシスタントです」 (regional manager-no assistant desu, ‘[you are] Assistant to the Regional Manager’). It’s very cool to see how this back and forth translates beautifully, and to see these actors execute it with the right timing and effect in a foreign language. Michael asks 「どうしてここにいるのだ」 (doushite koko-ni irunoda, ‘why are [you] here?’) and leads them in the radio exercises (ラジオ体操), saying 「じゃ、ラジオ体操をしましょう」 (jya, rajio taisou-wo shimashou, ‘well then, let’s do the radio exercises’).

Here’s another beautiful cultural point. These “radio exercises” are real, as every Mori no Ike villager knows, broadcasted over public radio and TV every morning, and are often done en masse at schools and some businesses. Pam’s then notes 「田中さんはみんなの健康を心配しています」 (Tanaka-san-wa minna-no kenkou-wo shinpai-shite-imasu, ‘Mr. Tanaka (=Michael) is concerned for everyone’s health’) in a conference-room reflection, and we see Stanley doing the crossword again.

After hurting his shoulder and cooling it with some oolong tea—the same bottle that was on Pam’s counter in the first scene—Michael reflects:

「今日はいい日でした。」 (kyou-ha ii hi deshita, ‘Today was a good day.’)
「いい仕事をした。」 (ii shigoto-wo shita, ‘[I] did good work.’)
「そう思う…かな？ はいはいはい！」 (sou omou… kana? hai hai hai!, ‘I think this way…? Yes yes yes!’)

The last line there is beautifully translated, capturing the essence of Michael in Japanese. As Japanese is a verb-final language, you literally say “blah blah blah I think” to mean “I think blah blah blah”, which may help explain the last phrase, 「そう思う」. Finally, the 「かな」 thrown in at the end turns the entire sentence, which was declarative up till then, into a question, which the bobble-head then answers. Brilliant!

In the final scene, Michael’s singing karaoke and Dwight yells 「かんぱい！」 (kanpai!, ‘bottoms up!’). The final credits list Sarah Sawyer and Hanna(h) Sawyer as producers… I wonder if they were actually involved with this Short or if they are also made up as well.

The details in the Short are great: the little Hello Kitties and origami, orchid plant on the reception desk (Japanese love orchids—or wait, maybe that’s just my grandfather), and all the copy paper that had 「コピー用紙」 (kopii-youshi, ‘copy paper’) pasted on. There’s a bonsai tree on Jim’s desk and his spoon is replaced with chopsticks.

If you want to get picky, of course, there are many rough edges… the incorrect use of 「ステープラー」 (how you would say “stapler” in Japanese) in lieu of 「ホッチキス」,² some text being poorly typeset, etc. But overall, this SNL Digital Short was obviously written by someone with a solid (albeit stereotypical) understanding of Japanese culture and strong intermediate Japanese skills. If the goal was simply only to play off of Japanese stereotypes, accurate Japanese wouldn’t have even been necessary, and so I really appreciate the effort that went into this. In addition, Steve Carell et al’s delivery in a language they don’t speak, in my opinion, is commendable.

“It’s funny because it’s racist,” in the best way possible. Bravo!

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1. Taipei find: a dictionary of Chinese-Japanese false cognates
2. Bailey won the Japanese Language Speech Contest
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As Google adds ten more languages to its machine translation service, it seems to be on its way to becoming the most convenient universal translator of the world’s popular languages. Google’s handling of languages of course isn’t perfect, however—in particular, I’ve been complaining to friends for a while about the weaknesses of Google’s handling of queries in Chinese character (漢字/汉字) scripts. In this post, I run some tests using Google’s Language Detection service to try to better understand its handling of Chinese character queries.

Background

Chinese characters have been used all across East Asia, most notably in Chinese, Japanese, Korean, and Vietnamese (the “CJKV”). Prescriptivist writing reforms in Communist China and Japan have simplified many characters, though. Some characters were simplified in the same way, some in different ways, and some in only one country but not the other. For more information, there’s Wikipedia or Ken Lunde’s CJKV Information Processing.

The problem

The issue comes up when you try to search for a word in Chinese characters which clearly came from one Chinese character-using language. From my experience, Google doesn’t consider which language you are a user of, based on the query, and returns many results in other Chinese character-using languages as well.¹

Take, for example, a query like “七面鳥”, meaning ‘turkey’ in Japanese. While all characters are very common in traditional Chinese (鳥 is simplified to 鸟 in China), the combination “七面鳥” is quite rare in Chinese. However, when you search for “七面鳥,” many of the first results are in Chinese and only two of the first ten results are in Japanese.

Does Google’s corpus not identify “七面鳥” as a primarily Japanese word? Google does indeed attest to this fact: searching for “七面鳥” and limiting to a certain language yields the following number of hits. A similar effect can be seen with Japanese words such as “芝生” (‘grass’) or “泥棒” (‘burglar’). The “Japanese on first page” column gives the number of results that are in Japanese which come up in a language-unspecified search from the US.

In a perfect world, I would like Google to identify the language that the query is in, and then weigh results that are in that language higher in the results list. So the issue comes down to one of language detection.

There are broadly two different approaches to language detection and, indeed, all natural language processing problems: parsing and counting. In this case, parsing involves trying to break apart the query into words and then computing how likely such a string of words is in each given language. Counting simply takes an inventory of the characters given and compares them to their frequencies in each language, computing how likely such a string of characters is in each language. Parsing is the “smarter” approach, but more difficult and computationally intensive.

Google was kind enough to give us an language detection AJAX service so we can get a sense for how their language detection works. This service also gives a “confidence” value on the detection result. For the rest of this entry, we’ll test some hypotheses against this service and conclude at the end.

Do spaces matter?

No. While spaces are sometimes used in Japanese and Chinese writing to represent word boundaries, especially around numbers and roman letters, they also are seen on the web to encourage line breaks. It would make sense for Google’s language detection service to ignore spaces in Chinese character queries and that does seem to be the case. All tests I ran with Chinese character queries gave the same result with same confidence with and without spaces in random places.

Does order matter?

No. This was slightly disappointing to see. I took the Japanese string “骨粗鬆症” (‘osteoporosis’, if you’re curious) and ran every permutation against the language detector and got the same results, including the same confidence values. This is a clear indicator that Google uses only counting, not parsing, in their parser.

Does repetition matter?

Yes. Now that it seems that Google does not use any parsing and only uses character frequencies in identifying the source language, let’s see how repetition can affect the detection service.

First, I took some Chinese character strings and ran them through the detection service with different numbers of repetitions, e.g. “参加”, “参加参加”, “参加参加参加”, “参加参加参加参加”… The queries I used were the following:

For each token type, the detection service made up its mind quite quickly. Its confidence, however, was more interesting.

Each of the confidence values dips sharply after three, five, or ten repetitions. Note, however, the length of the tokens which dipped at each of those points. I interpret this to mean that there is a different parser for less than ten characters and ten or more characters. However, the detection service did not change its answer after this point on any of the tokens.

Second, I took two characters, “簡” and “体,” and crossed different numbers of them together to see how that would affect the language detected. Note that “簡” is used in traditional Chinese and Japanese, while “体” is used in simplified Chinese and Japanese.

Conclusion

For Chinese character-based languages, Google’s language detection algorithm uses simple counting rather than parsing, identifying languages by looking at the frequency of characters rather than the frequency of words. As such, the algorithm simply acts as a script detector, not a language detector. Moreover, as a simple counting method is used, duplicating characters used in one language but not another can very easily skew the resulting output.

As a trivial aside, it seems that Google’s algorithm is slightly different for strings less than ten characters, as can be seen in a dip and then rise of confidence values after ten characters.

Related posts:

1. Taipei find: a dictionary of Chinese-Japanese false cognates
2. Bailey won the Japanese Language Speech Contest
3. Setting Language Research to Music

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She never let me see or listen to the speech, though—now I’m curious.

I’m very proud of her. ^^

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2. Taipei find: a dictionary of Chinese-Japanese false cognates
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