Tomo-Sensei is a Japanese grammar AI explainer built into Yomimaru's reading interface. Highlight any sentence in any text you are reading — from Yomimaru's library or a document you imported — and Tomo-Sensei returns a real-time structural breakdown: what each particle does in that sentence, where embedded clauses begin and end, what the dropped subject is, and what the conjugation chain means. It works on any text, not just pre-annotated content.
The Gap That Grammar Books Cannot Fill
Every serious Japanese learner has at least one grammar reference: Genki, Tae Kim, A Dictionary of Japanese Grammar, or Bunpro. These are genuinely useful tools for learning what te-form is, what the passive construction looks like in isolation, and what conditionals mean in theory.
The gap opens when you sit down with a real Japanese sentence and cannot parse it — even though you know all the individual grammar patterns it contains. A sentence like:
「長年その問題を避けてきたとされる政府の対応が、ようやく見直されつつあるとの報告が出た。」
You might recognise every word. You might recognise passive, te-form, and quotation particles. And yet the sentence is opaque, because the grammar is not the problem — the structure is. Which clause modifies which noun? Who is the actual subject? Where does the main clause start?
No grammar dictionary answers this. They all describe patterns; none of them parse a specific sentence and tell you how those patterns fit together in this instance. That is what Tomo-Sensei does.
What Actually Makes a Japanese Sentence Hard to Parse
Two features of the language do most of the damage, and neither of them is a grammar point you can look up.
The verb arrives last. English tells you what is happening early: Ken ate the apple. Japanese holds it back — 「ケンはりんごを食べた」 gives you Ken, then the apple, and only then 食べた. Across five words that costs you nothing. Across a sentence that runs four lines, it means carrying every noun and every particle in working memory without yet knowing what any of them are for, because the word that assigns them their roles has not arrived. Lose one particle on the way through and you cannot recover it at the end.
Modifiers come before the noun, and nothing marks where they start. English hangs a description off the back of a noun and announces it with a joining word: the book that my friend read yesterday. Japanese puts the whole clause in front and announces nothing.
昨日友達が読んだ本
直訳 Word by word
Yesterday friend read book.
Four words, and only the last one is the thing being talked about. No "that", no comma, no signal that a clause has begun — you find that out retroactively, when you hit 本.
Now nest two of those inside each other, give each its own subject and its own verb, and drop the outer subject because the context implies it. Every word is one you know. The sentence is still opaque. That is the state Tomo-Sensei is built for.
What Tomo-Sensei Actually Tells You
Particle disambiguation in context
Japanese particles carry the grammatical structure of a sentence — は marks topic, が marks subject, を marks object, に marks direction or receiver. But in complex sentences, it is not always obvious which noun is connected to which verb via which particle.
Tomo-Sensei identifies what each particle is doing in the specific sentence you highlighted — not in the abstract, but in context. It tells you which が is the subject of which embedded verb, and which は is contrasting which items.
Embedded relative clause structure
A single noun can be modified by three stacked clauses, each with its own verb and its own particles. Tomo-Sensei marks where each one begins, which noun it ultimately lands on, and what relationship it establishes — the retroactive work you would otherwise be doing by hand, twice over, on a sentence with two of them nested.
This is the single most common reason intermediate learners cannot parse N2 and N1 sentences. The clauses are all recognisable in isolation; the stacking pattern is not.
Dropped subjects and implicit speakers
Japanese regularly drops the subject of a sentence when it can be inferred from context. In a long text, the implicit subject can shift mid-paragraph without any explicit pronoun. Tomo-Sensei identifies what the dropped subject is based on the surrounding text context, so you know who is doing what.
Passive and causative constructions
Japanese passive (〜れる/〜られる), causative (〜せる/〜させる), and causative-passive (〜せられる/〜させられる) stack onto verbs and change who is acting on whom. When these appear in formal written contexts — newspapers, business reports, academic writing — they are compressed and easy to misread.
Tomo-Sensei identifies which construction is active and explains who the agent and patient are in that specific sentence.
Conditional nuances
Japanese has four main conditional forms — 〜たら, 〜ば, 〜と, 〜なら — each with subtle differences in implication and usage. Standard grammar explanations describe the difference in theory. Tomo-Sensei identifies which conditional is present and explains what specific implication it carries in that context: is this a neutral if-then? A hypothetical? An expectation? A recommended condition?
Formal written register patterns
N2 and N1 texts use written-language grammar patterns that almost never appear in spoken Japanese: 〜にすぎない, 〜にもかかわらず, 〜をめぐって, 〜に基づいて, 〜に過ぎない. These compound expressions function like single grammatical units.
Tomo-Sensei identifies them as units, explains what they mean as a whole, and places them within the sentence structure so you understand how they connect to the main argument.
How Tomo-Sensei Differs from Other Grammar Tools
| Grammar dictionaries | Bunpro | DeepL / Google Translate | ChatGPT (standalone) | Tomo-Sensei | |
|---|---|---|---|---|---|
| Explains patterns in isolation | ✓ | ✓ | ✗ | ✓ | ✓ |
| Works on a specific sentence | ✗ | ✗ | Translation only | ✓ | ✓ |
| Integrated into reading interface | ✗ | ✗ | ✗ | ✗ | ✓ |
| Works on custom imported text | N/A | N/A | ✓ | ✓ | ✓ |
| Preserves reading flow | ✗ (switch apps) | ✗ (switch apps) | ✗ (switch apps) | ✗ (switch apps) | ✓ (inline) |
| Context-aware (reads surrounding text) | ✗ | ✗ | ✗ | Sometimes | ✓ |
The critical difference is the last two rows. Every other tool requires you to leave what you are reading, copy the sentence somewhere else, and then return — breaking the reading flow every time. Tomo-Sensei is integrated directly into the Yomimaru reading interface, so the explanation appears alongside the text without any context switch.
How to Use Tomo-Sensei Effectively
When to use it:
- You have read a sentence twice and still cannot determine who is doing what to whom.
- You recognise all the vocabulary but cannot figure out how the clauses connect.
- You see a formal written construction you have not encountered before.
- You want to confirm whether your grammar reading of a sentence is correct before moving on.
When not to rely on it:
- As a substitute for reading. Using Tomo-Sensei on every single sentence prevents fluency from developing. Use it for sentences that genuinely block comprehension.
- For vocabulary lookups. Tap any individual word for the built-in dictionary — Tomo-Sensei is for sentence-level structure, not word meanings.
The most effective workflow:
- Read a sentence. If you can extract the meaning, continue.
- If the sentence blocks you, attempt to parse it yourself first — identify the main verb at the end, work out the basic subject-object-verb frame.
- Then highlight and trigger Tomo-Sensei. Compare its breakdown to your attempt.
- Re-read the sentence with the correct structure in mind. Your brain internalises the pattern.
This active-then-verify loop builds grammar intuition faster than looking up every sentence passively.
By JLPT Level: What Tomo-Sensei Helps With Most
N4–N3 learners: Embedded relative clauses, dropped subjects, te-form chains connecting multiple actions, basic passive recognition.
N2 learners: Stacked modifying clauses in formal texts, perspective markers and hedging language, compound conditional structures, formal written vocabulary that functions as grammatical units.
N1 learners: Abstract essay structures where the author's argument is layered across multiple paragraphs, formal honorific and humble form chains in business contexts, academic hedging with nominalisation, sentence-final nuance particles in literary prose.
Tomo-Sensei as a Grammar Pattern Database
Every explanation Tomo-Sensei generates is tied to the sentence you highlighted. Over a reading session, you may encounter the same construction three or four times across different sentences. Each time Tomo-Sensei explains it, you are building a mental corpus of that pattern in real usage — not in isolation.
This is fundamentally different from studying grammar lists. A list tells you what a pattern is; only reading native Japanese content shows you what it does, in the sentence shapes it actually turns up in. Tomo-Sensei closes the gap between the two.
Further Reading
- JLPT N3 reading comprehension strategies — applying grammar knowledge to timed reading passages at the N3 level.
- JLPT N2 reading strategies — advanced sentence structures and formal register that Tomo-Sensei helps navigate at N2.
- Master Japanese vocabulary through context — vocabulary and grammar reinforce each other when you build both through real reading.
Test Yourself: The Adaptive Grammar Challenge
Reading about clause structure is not the same as spotting it under time pressure. The challenge below drops a particle or a conjugation out of a real sentence and asks you to put it back — five questions, adapting to the level you clear. If you find yourself guessing on the embedded-clause items, that is precisely the gap Tomo-Sensei is there to close while you read.