“Verbs: they’re the proudest—adjectives you can do anything with, but not verbs—however, I can manage the whole lot of them! Impenetrability! That’s what I say!” – Humpty Dumpty
We spend a lot of time skimming. In fact, for language in general, studies suggest we take a ‘good enough’ approach, satisficing rather than maximizing.
You might think of extensive reading (tadoku [多読]) or extensive listening when I mention all this: Consuming a lot of material, trying to get a ‘good enough’ understanding and moving on, without looking up words.
Research shows that while great for motivation and necessary for well-rounded understanding, the problem with this is that it’s slow and inefficient, and requires designing or finding material that doesn’t exceed your level too much: it’s best used for reinforcing what you’ve learned through deliberate study. Loosely speaking, study is typically the fast-mapping process, while contextualized usage is the slow-mapping, or extended-mapping, process.
So a combination is best, but most combinations tend to be too efficient and studying too much, or too inefficient and studying too little, with an uncomfortable gap between study and authentic usage. It’s disheartening when you spend so much time studying, and it feels like real materials and enjoyment are just out of reach. You get burnt out. But when you try to skip the gap, the struggle to comprehend or the inefficiency of just ‘moving on’ from difficult items is subpar.
I’ve come up with various methods to overcome this, first giving more weight to deliberate study, gradually increasing usage, and learning in targeted batches to keep study/usage close together, but I think I’ve found something better, that I hope to streamline over time. It’s a continuation of my ideas such as ‘soft monolinguality‘ or ‘incremental immersion‘.
My new proposal is for we listless self-students who often can’t be bothered to study sentence cards, yet studying words in isolation feels too dull. But we still want efficient study to complement our media consumption, ideally.
I propose you take a ‘good enough’ approach to studying Japanese, in addition to using it. A gist-based approach to studying for immediate usage, to be precise.
While I have created resources to employ a laid back, satisficing approach to studying, here I don’t mean to be slapdash with your studying, but to specifically study:
- a) the least amount of items that will
- b) allow you to comprehend the least amount of target material necessary to
- c) understand its core content
This emphasis on study is important, because it takes years of experience or strong doses of deliberate practice to skim and scan at a good speed with decent comprehension.
With the proposed approach, we can minimize the gap between study and usage to just how long it takes to learn the minimal number of items for gist.
Note that I said target material. We want to be very specific. Right from the onset of your Japanese learning, you seek out interesting media, such as a chapter of a light novel (or a whole light novel), a news article, a television episode, etc., and tailor effortful study around the goal of just getting the gist of it, quickly and with minimal effort during actual media consumption. (Of course, if you’re really just starting out, first learn kanji and words together.) This is why, as I’ve said in the past, generalized frequencies are a bit problematic… they’re too general, and may not apply well to your input/output goals.
Skimming is for gist; scanning is for extracting specifics. Gist is gleaned by attending to the meanings of multiple words at a time, primarily, rather than syntax. The ‘good enough’ approach to language suggests that full syntactic processing is only used when semantic processing needs it.
If the idea of ‘good enough’ bothers you because you want to be perfectly native-like, keep in mind that you will never be native.
I don’t mean that you can never be indistinguishable from a native in performance, I mean that your approach to learning should be piecewise—quantum bits—because you will always be a mixed-language user whose usage differs from a native’s due to this mixing. Note that I said ‘user’, not ‘learner’. In reality, the goal is to be a successful L2 user, fluent in what you’ve learned, no matter your level.
The process is always additive—rather than biasing attention to the incomprehensible, everything you learn is a delightful addition to your mental toolkit.
So what are the core items to learn from materials for getting the gist? What do we study that isn’t too isolated and dull, nor too lengthy?
The answer to both of these questions is predicate-argument structures (PAS) [述語 ・項構造]. This is a fancy term for verbs and what they connect to, generally subjects and objects. In the sentence ‘I skipped school’, the PAS is ‘I’ (subject) ‘skipped’ (verb) ‘school’ (object).
If you look at a sentence in terms of its syntactic dependencies (an adjective describing a noun is ‘dependent’ on the noun, etc.), the PAS represents semantic relations as well, indicating the roles of the terms. PAS are very useful for information retrieval and other areas of natural language processing.
Let’s take a look at verbs, for a moment.
“Verbing weirds language.” — Calvin & Hobbes
At the core of every sentence is a verb upon which everything depends, directly or indirectly, in terms of dependency grammar (Google’s Parsey McParseface uses this distinctly non-Chomskyan grammar), where every word in a sentence is dependent on another—except the root verb, which isn’t dependent on anything. It’s called ‘verb centrality’.
Doing the hard work of every sentence is this main verb, even if it’s indirectly felt, or implied through context rather than explicitly in the sentence, as sometimes seen in speech. In the obsolete ‘generative grammar’, I believe they used to call it a ‘matrix verb’.
- In ‘construal theory’, primary phrases are root verbs and their subjects.
- Contextualized action verbs (‘threw the ball’) activate motor sequences (throwing something) when we process them.
- Verbs, of course, are frequent in programming. Although Java has had issues.
- Short action verb phrases are quite useful for learning to think in Japanese.
- Calling ‘heat’ a noun instead of a verb had repercussions, due to how language shapes our thoughts.
Even cooler: Japanese is a verb-friendly language. Japanese verbs are acquired relatively more quickly in Japanese, used at a higher frequency, and acquired earlier. This is because a verb always occurs at the end of a Japanese sentence or PAS, and verb arguments (e.g. the subject ‘I’) are often omitted. These traits in languages like Japanese factor into how the language is learned and used, so that the verbs are more central.
Tae Kim has suggested that the farther you get from the main verb of a sentence, the more extraneous the information becomes. This could be because dependency distance (distance between a word and what it depends on) typically increases the difficulty.
“Whenever the literary German dives into a sentence, that is the last you are going to see of him until he emerges on the other side of his Atlantic with his verb in his mouth.” — Mark Twain
Because Japanese is a head-final language, with verbs coming after their complements and the root verb at the end, studies suggest that the complements of a verb play a critical role early in Japanese sentence processing, narrowing predictions. Supposedly the head-final nature of Japanese affects overall perception, as well. (See also Senko Maynard on the agent-does structure.)
In the past I’ve discussed kotodama (言霊), the ‘word-soul’ in Japanese, where kanji are living objects; if this places an emphasis on nouns, then I could say that verbs are like ‘power words’.
The ‘bare verb’ (presented by itself) is typically enough for Japanese children to learn, because as noted, subjects are often dropped in Japanese; so the morphology (the form, such as te iru as -ing verb) is often relied on for success.
So, while the subject isn’t all that useful for learning verbs, specifically, Japanese verbs are more general than in English, and the context added by objects (‘ball’ in ‘threw the ball’) is very useful.
Still, fast-mapping verbs in general is harder than nouns, so it’s good to use spaced retrieval (e.g. Anki) for learning verbs rather than relying on usage. Using Anki to study is like fast-forwarding the fast-mapping. It’s either Anki or robots.
Let’s put all this together to implement our ‘good-enough’ approach.
For us, it’s: 「あきらめが悪いな」 – Why don’t you give up?
We want to study the minimal core Japanese needed for gist, narrowing down our targets for reading and listening tasks. We know where to locate the most essential aspects of sentences: at the end, with the root verb. We want at least one argument, for context, such as the object. Subjects are a bit less important, as noted, but also, as pronouns (‘I’, ‘she’, ‘Hiroshi’, etc.), they’re often repeated and relatively small in number, so they’ll be easier to recognize.
So what do we do? We ‘normalize’ the sentences, compressing them to just the predicate-argument structures, which connects verbs to their complements whatever the distance between them.
Instead of every sentence being a lengthy, special unique snowflake, we extract the essential aspects. In research, the best sentence compression and translation methods use bunsetsu, dependency grammar, and PAS, making the results more readable.
What’s nice about this is you’re studying chunks, which I’ve discussed before in the context of language processing and collocations. Learning these improves fluency, and they generally need to be deliberately studied.
Often the PAS will recur, so learning a PAS can help you skim multiple sentences, and you can flesh out your understanding as you see verbs used in various ways and arguments are mixed and matched. And of course, you’ll be acquiring more and more PAS.
Remember that I said for gist, semantic processing is ideal? PAS let us go beyond the surface to the deeper semantic structures. They’re basically ‘semantic frames’ for events. They make up the propositions used in Word Grammar and elsewhere.
Some have even suggested that predicate-argument structures are the core of our mental representations, upon which modern languages are mapped.
They’re thematically related clusters of words, rather than semantically related clusters. Thematic clusters are easier to learn than semantic, because when words (or kanji) are closely related in meaning, they interfere with one another.
In document summarization research, predicate-argument structures form topic themes which can be used to identify key areas to focus on.
They also form ‘generative’ language, allowing you to produce important messages (think ‘survival phrases’ using verbs like ‘eat’, ‘drink’, ‘sleep’, etc.). So PAS are good for production exercises, also, such as tweeting.
“The whole of nature, as has been said, is a conjugation of the verb ‘to eat’, in the active and passive.” — W.R. Inge
We might also correlate PAS with verb-noun collocations, which have often been a target of language learning research. Modified dictoglosses (a dictogloss is a dictation/output task) have been shown effective for learning these by underlining them in target texts.
We can differentiate a PAS and a verb-noun collocation in that the latter describes common patterns such that they tend to be processed like a single unit, while a PAS is any instance of predicates and their arguments.
It seems learning these chunks as wholes is best, to avoid ‘cross-association’ mistakes in matching the separated words.
Recall that skimming is for gist, scanning is for specifics. In a sense we can treat the arguments, typically nouns, as the concrete specifics to scan for, and the verbs as the general gist, with the root verb of the sentence acting as a kind of router.
Or you can look at verbs/predicates as functions (as linguists sometimes do since the predicate calculus of Frege), which take arguments as input and output coherent events: verb(subject, object) → subject-object-verb, a predicate-argument structure. We primarily want the skill to parse this. (I guess we could say the main verb is a higher-order function? Perhaps a metafunction? Maybe we can throw in case markers as type hinting (since particles can be omitted colloquially)? Or let’s not.)
And as noted before, for abstraction, nouns can be seen as data structures/objects, verbs as functions/algorithms. Functional programming style (as opposed to OOP) tends to be verb-biased, like Japanese. Of course, Japanese is a hybrid morphographic/phonographic system with the best of both worlds, and these days programming languages are hybrids of functional and object-oriented styles, as seen with Clojure. Perhaps this is why Clojure is so expressive. But I digress.
If we want to add a layer to incorporate more scanning, we want to look at ‘keywords’, rather than just PAS. The keywords of a text are the nouns with the most dependents. These help summarize the content of documents. You might relate this to valency, which refers to how many arguments a predicate has.
So the keywords and PAS are the potential core targets for getting the gist of any material you want to consume.
Now, how do we go about extracting and studying them?
First off, just knowing what I’ve told you, you can take any resources you already have, and focus initial study on the verbs and their complements.
It would be nice if we could just select the last couple of pieces of any Japanese sentence and voila, but the arguments we want aren’t commonly so well-placed next to the final verb. For example:
2つの薬品を1対3の割合で混ぜた
The object ‘chemicals’ is separated from ‘mixed’ by ‘at a ratio of 1 to 3’.
But we can also automate this process with ChaPAS. ChaPAS will take any Japanese text input and output a text of the dependencies and predicate-argument structures. I’ve written a short tutorial on how to use it here.
It doesn’t require programming knowledge, you’ll just need to install a suite of tools and copy a few command-line statements. However, if you know regular expressions or other fancy find/replace techniques and the like, this will help clean up the output. In the future, I will try and create tools to streamline the process of listing the PAS, and bulk producing pretty diagrams out of the ChaPAS output. Until then, I will create some resources, to be uploaded shortly. Update: For now I’ve modified this script to output PAS.
Another bonus of ‘normalizing’ sentences for media is that we can share decks and other resources that contain only these, without copyright fear. You can’t copyright words or short phrases; and because they’re compact and generalized, we don’t need the original audio: we can use WWWJDIC’s audio for each word, or text-to-speech (TTS) to capture the PAS in a single piece. As for copyright, see also Google’s successful ‘snippets’ defense for its book-scanning, and corpora of copyrighted works that only use KWIC.
Note on manipulating the PAS: Keep in mind it’s typically SV (subject-verb), OV (object-verb), or SOV (subject-object-verb), in general (with an indirect object and direct object, it’s usually SIOV). Occasionally you’ll see OSV (object-subject-verb), because word order is quite free in Japanese as long as the postpositions and case markers (the particles) are maintained. But it’s best to keep things transfer-appropriate, the items you study reflecting authentic usage. To digress, another rarity is crossed dependencies.
Ideally what we want to focus on are PAS, supplemented by keywords. Mainly we want to focus on the primary PAS (from the root verb), but this is a refinement that I will try to add in a tool, later, along with a particular automated summarization technique using keywords that I have in mind, informed by my own ideas and research I have read.
We want to focus on the nominative (が)—the subject, the accusative (を)—the object, and the dative (に)—the indirect object. I would prioritize accusative, then dative, then subject, but it’s not essential to prioritize anything. For names, we also have the ability to detect Named Entities with ChaPAS and CaboCha. Another refinement.
With ChaPAS, as I noted, you can end up with a list of the PAS, or diagrams, though diagrams are more involved. For our gist-based method, the diagrams are really just supplements, it’s the isolated PAS in text form that we want:
学校を ← 休んだ (skipped school)
薬品を ← 混ぜた (mixed the chemicals)
Notice I kept the inflection. This is how the words are actually used, and recall that we want things to be transfer-appropriate, learning the conjugations in context on both cards and in usage. ChaPAS stores the dictionary form (lemma) in the output, so we can still make use of this in various tools.
For the format of cards, you can just stick the PAS on the front of an Anki card with the meaning on the back, but the multiple words might be too hard a recognition task. You could list the kanji meanings on the front with the PAS, offsetting the difficulty by the hints from kanji meanings, using word formation principles to put them together. But that might make it too easy.
So to modulate the difficulty/ease, I recommend using the add-ons I created here for just this purpose.
Essentially, you’ll have a mixed recognition/production card with those add-ons, where you place the kanji-jumbled version of the PAS on the front, along with its meaning and the shuffled meanings of the kanji, and you recall the properly spelled version of the PAS.
I also recommend putting the audio on the front with readings—sound isn’t as important for reading Japanese as it is in English, as Japanese is morphographic. Additionally, if we want listening tasks, rather than reading tasks, then we want audio (with Japanese text as supplement, as with subtitles) to be our cue, making the task transfer-appropriate. In fact, I think PAS-only Japanese subtitles would be good to try. Perhaps mixed with adaptive subtitles. You can actually use the semantic content of PAS as a shared anchor between sentences, perhaps, say, a Japanese and an English sentence.
But that’s another refinement.
Once we use the PAS, preferably at least the root PAS for each sentence, and possibly keywords to build a foundation, after just 2-3 reviews for each batch you’ve extracted from material, we can consider the batch well-learned and start consuming media, focusing on just the gist, identifying the small fraction from each sentence that we’ve studied as our goal, and using it to infer the rest—that is, the PAS isn’t the endpoint, it’s the bridge to the rest of the media. For reading, since the PAS are conjugated, you might re-insert and highlight them, a form of ‘input enhancement’.
I don’t advocate it (yet), but a seductive notion is that since spacing is a complement to testing (the ‘retrieval’ in spaced retrieval), which is independent and equal or superior in effect, PAS make cramming a possibility—ultra-fast-mapping in Anki followed by a heavier emphasis on incidental learning through usage, perhaps retiring cards quickly to focus on media and cull your decks. Or a kind of spaced cramming, rather, since even microspacing in a single day is superior to cramming. ‘Preloading’ vocabulary rather than ‘prelearning’ it.
This would require a substantial media consumption rate and a high negative capability, as the poet Keats called it. If you did something like this, you could perhaps use a single deck that only ever contains the gist cards for a single text or episode. Cards can be suspended, or automatically emptied with this add-on and the periodic use of the Empty Cards option in Anki.
It’s up to you whether you want to place multi-argument PAS on the same card, or split them up. That is, if you have subject ← object ← verb, you could make a subject ← verb card and an object ← verb card. I think it’s probably best to keep it all together.
If you don’t intend to share materials and/or they’re not copyrighted, then you could include the original sentence meanings (not the sentences) on cards, focusing on the aspect of the meaning which captures the PAS meaning. Don’t worry, you won’t accidentally memorize the entire sentence meanings, ruining the novelty. If our memory was that amazing, we wouldn’t need much studying, would we?
I’ve been focusing on PAS, but for keywords, rather than looking for some kind of dependency analysis using ChaPAS or CaboCha, the simple version (as with simply identifying verbs and objects on sentences) is to pick an adjective-noun combination from sentences that contain them. These are easier to learn (this is true of adjective-noun kanji compounds, also). You could perhaps extend PAS that have noun arguments to include any adjectives for the noun. Another refinement.
Another use of PAS is to create ‘thinking in Japanese’ cards.
I’ve presented to you the justification and implementation, and I think you can take what you want from it and make it work, but I do encourage you to look into ChaPAS and other tools, and I believe summarization can make things even better, by narrowing the number of PAS even further, and giving you short extracts of target materials. That is, document analysis can give ideal sentences to compress and prioritize for study of the document as a whole, or just the extract.
How you place this gist-based approach in your overall regimen is up to you.
I have found a few summarization tools, and in my own studies and brainstorming have discovered actionable methods I intend to share in the future. In the meantime, you can look into ledes for Japanese news articles, just picking the first sentence or two. Likewise for paragraphs and topic sentences. However, this depends on how inductive the style is. Here’s a corpus of lead lines from web articles that have been annotated with PAS–the readme is very helpful, and the PAS annotations are in the .KNP files which can be opened with a text editor. But stay tuned for this and other resources…
Oh, and Systemic Functional Gistics comes from Systemic Functional Linguistics; the metaphor for verbs as functions; a functional approach to studying for immediate use; and of course, skimming for gist.
