Datasets:

NagaYu
/

downstep-bench

---
language:
  - ja
license: bsd-3-clause
license_link: https://opensource.org/license/bsd-3-clause
pretty_name: Downstep compound pitch-accent benchmark (listed / unlisted / hard)
task_categories:
  - token-classification
tags:
  - japanese
  - pitch-accent
  - prosody
  - tts
  - openjtalk
  - compounds
  - generalization
  - data-splits
  - multi-label
annotations_creators:
  - machine-generated
language_creators:
  - expert-generated
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: dev
        path: dev.jsonl
      - split: test
        path: test.jsonl
---
# Downstep compound pitch-accent benchmark

**Japanese noun compounds, their mora segmentation, and every accent their source
dictionaries attest. Built so that "the model has never seen this compound" is a
condition you can actually turn on, rather than a claim you have to trust.**

> **No human annotation is present in this release.** Every accent label here is
> dictionary-derived. `docs/ANNOTATION_GUIDELINES.md` ships the protocol, the CSV
> format and the agreement statistics for collecting native-speaker judgements on
> novel compounds — but that collection has not happened, and nothing in this
> repository should be read as if it had. There are no annotator IDs, no agreement
> scores and no human-adjudicated items.
---
## Sources and licences
Two independently produced lexicons. Downstep reads the copies installed by
`unidic-lite` and `pyopenjtalk` and never fetches anything over the network.
| Source | Holder | Licence | Reached via |
|---|---|---|---|
| **UniDic 2.1.2** (unidic-mecab) | The UniDic Consortium / NINJAL | GPL **/** LGPL **/** BSD 3-Clause, tri-licensed — **Downstep relies on the BSD option** | `unidic-lite` |
| **NAIST-jdic** (Open JTalk's system dictionary) | Nara Institute of Science and Technology | BSD 3-Clause | `pyopenjtalk` |
**Attribution both licences require**, reproduced in full in [`NOTICE`](../NOTICE) and
required of anyone redistributing these rows:
- Retain the copyright notice, the list of BSD 3-Clause conditions and the warranty
  disclaimer.
- Do **not** use "The UniDic Consortium", "NINJAL", "Nara Institute of Science and
  Technology" or their contributors to endorse or promote anything derived from this
  data without prior written permission. Downstep makes no such endorsement claim.
The BSD option is what makes the derived rows redistributable at all: under the GPL
option a redistributed derivative would have to be GPL, which is why the choice is
stated explicitly rather than left to the reader.
The tooling in this repository is Apache-2.0. The data rows below are governed by
BSD 3-Clause through their sources.
### What is redistributed, and what is not
**Redistributed:** derived rows — surface, reading, mora segmentation, the set of
attested accents, the component decomposition, and the split/stratum labels.
**Not redistributed:** the dictionaries themselves, in compiled or CSV form; any
audio; any human judgement. `downstep.lexicon.load_lexicons()` rebuilds the local
cache (`data/lexicon/*.jsonl`, git-ignored) from the installed packages in ~20 s.

Measured in the cached dump on 2026-09-03 (`make lexicon`, then a count over the
JSONL):

| | UniDic 2.1.2 | NAIST-jdic |
|---|---:|---:|
| entries | 751,705 | 788,331 |
| distinct surfaces | 568,123 | 461,975 |
| noun entries | 277,189 | 455,743 |
| distinct noun surfaces with an accent | 258,371 | 418,671 |
| noun entries recording **more than one** accent | 21,568 | 0 |
| noun surfaces whose accents disagree **across** entries | 24,844 | 5,854 |

Those two right-hand columns are the raw material for the multiple-answer field.
NAIST-jdic records one accent per entry, so its variation only appears when two
entries share a surface; UniDic records variation inside a single entry
(`"0,4,3,1"`, CSV-quoted — a naive `split(",")` both mis-counts the fields and
destroys exactly these rows).

---

## What one row is

A noun compound that satisfies **both** of:

1. It is a lexicalised entry in NAIST-jdic, so a lexicographer assigned it an accent.
   That is the gold.
2. It decomposes into **two or more** morphemes whose readings compose back to the
   compound's own reading, with 連濁 / 促音化 accounted for. That is what makes it a
   composition problem rather than a lookup.

Cut points for (2) are constrained to a morphological analyser's boundaries. The
analyser is **UniDic** while the gold is **NAIST-jdic** — deliberately a different
dictionary, so the decomposition is not read off the answer. Without that constraint
the decomposition collapses: a naive dynamic program over dictionary surfaces splits
ファインマン into ファ+イン+マ+ン, because nearly every single kana is itself an entry,
and reading-consistency alone cannot reject it.

## Fields

| Field | Type | Description |
|---|---|---|
| `surface` | str | The compound |
| `reading` | str | Katakana reading, devoicing marks and interpuncts removed |
| `moras` | list[str] | Mora segmentation; `len(moras) == n_moras` is enforced at build time |
| `n_moras` | int | Mora count |
| `gold_accents` | list[int] | **A set, not a value.** `0` = 平板; `k` = nucleus on mora `k`, 1-indexed |
| `has_variation` | bool | `len(gold_accents) > 1` — the dictionaries attest more than one accent |
| `components` | list[obj] | Per component: `surface`, `reading`, `accent`, `n_moras`, `goshu`, `acon_type`, `in_lexicon` |
| `n_components` | int | Number of components (always ≥ 2) |
| `goshu` | str | 語種 of the compound, taken as the modal component value (和 / 漢 / 外 / 固 / 混) |
| `script` | str | `kanji` / `katakana` / `mixed` |
| `is_proper` | bool | The NAIST entry's POS is 固有名詞 |
| `all_components_in_lexicon` | bool | Every component was found in a source lexicon |
| `split` | str | `train` / `dev` / `test` |
| `hard` | bool | Test-only: **no** component of this compound occurs in any training compound |
| `source` | str | `naist-jdic`, or `naist-jdic+unidic` when the accent set draws on both |

### `gold_accents` is the multiple-accepted-answers field
Japanese pitch accent genuinely admits more than one correct answer for many words,
and the dictionaries record that. Scoring against a single "true" accent would
manufacture errors out of real variation. So:
- **Score membership**, not equality: a prediction is correct if it is *in*
  `gold_accents`. `downstep.metrics.nucleus_exact_match` takes a set.
- `has_variation` marks these rows so they can be reported as their own stratum,
  because a system that always guesses the majority variant looks better on them than
  it deserves.
- This field records **lexicographic** variation between and within dictionaries. It
  is not, and must not be presented as, a survey of how speakers pronounce these
  words.

---

## Splits, conditions and strata — three different things

**Splits** partition the rows. Assignment is `sha1(surface)` thresholded, not a
shuffled seed, so adding new candidate words never moves an existing word between
splits and any machine reproduces the same partition.

| split | share | rows |
|---|---|---|
| `train` | 79.9% | **71,544** |
| `dev` | 10.0% (calibration and abstention threshold) | **8,959** |
| `test` | 10.1% | **9,036** |

Total: **89,539** compounds.

**Conditions** are not splits. `listed` and `unlisted` are the *same* test rows
evaluated twice, with the dictionary's whole-word entry present and then ablated at
query time. See [`docs/METHODOLOGY.md`](../docs/METHODOLOGY.md) for why the ablation
must be rebuilt per target — batching corrupted 47.7% of targets in the pilot.

**`hard` is a partition of the test set.** Two nested flags:

* `hard` (818 rows, 9.0% of test) — the
  compound's **final** morpheme never occurs in any training compound. The final element
  (後部要素) governs compound accent in the classical rules, so this is the linguistically
  meaningful generalisation test.
* `hard_all` (105 rows) — *no* component occurs in training. Stricter,
  but too small to carry a claim; kept as a stratum.

Reported separately, never averaged into the headline: a pilot found a memorisation-shaped
model gained **nothing** on unseen final components (58.3% vs 58.3%). The trained model does
better than that — but the split exists so that can be checked rather than assumed.

| stratum field | why it is a stratum |
|---|---|
| `is_proper` | Proper nouns are a different problem with different conventions; they are 66% of these rows (59,430 of 89,539) and are **reported on their own**, never folded into a single number |
| `goshu` | 和語 / 漢語 / 外来語 compounds follow different accent regularities |
| `n_components` | Two-part compounds are much easier than three-part ones |
| `n_moras` | Longer compounds have more positions to get wrong |
| `has_variation` | Rows with more than one accepted answer |
| `script` | Katakana (largely loanword) compounds behave unlike kanji ones |

Row counts, from `benchmarks/stats.json`:

| stratum | counts |
|---|---|
| script | kanji 55,237 · mixed 27,290 · katakana 7,012 |
| 語種 (`goshu`) | 和 34,760 · 固 25,085 · 漢 23,313 · 外 4,443 · 混 604 · 記号 570 · unknown 764 |
| components | 2 → 70,419 · 3 → 16,286 · 4 → 2,539 · 5+ → 295 |
| moras | 4 → 21,228 · 5 → 19,522 · 6 → 18,884 · 7 → 11,112 · 8+ → 18,793 |
| noun type | proper 59,430 · common 30,109 |
| more than one attested accent | **1,030** |
| every component in a lexicon | 89,539 (all of them — decomposition requires it) |

---

## Limitations, stated plainly

- **The gold is a dictionary's opinion.** Dictionaries disagree with each other and
  with speakers. Where they disagree with each other the row keeps both answers; where
  they disagree with speakers, this data cannot tell.
- **The variety recorded is a broadly Tokyo-based standard.** That is what these two
  lexicons encode, so it is a scoping decision forced by the available data. It is
  **not** a claim that other regional or generational accent systems are incorrect, and
  a Downstep prediction is not a correction of anyone's speech.
- **These are all words that exist in a dictionary.** The target application is
  compounds that do not. The ablation device is what bridges that gap, and its limits
  are argued — not waved away — in `docs/METHODOLOGY.md`.
- **Reading-consistent decomposition rejects real compounds.** The rendaku model covers
  連濁 and 促音化 but not 連声 or stem alternations (雨 → あま, 酒 → さか), so compounds
  built on those are dropped rather than mis-segmented. That is a coverage limitation
  the numbers do not show.
- **No speech.** No F0 contours, no durations, no speaker information. Accent here is
  an integer, not a measurement.
- **Not a pronunciation dictionary.** Do not use these rows as a reference for how a
  word "should" be said. For that, consult NHK日本語発音アクセント新辞典 or
  明解日本語アクセント辞典.

## Out-of-scope uses

- As an authority on correct pronunciation, in education or assessment.
- To evaluate or rank speakers, or to flag any pronunciation as an error.
- As evidence about regional variation, which it contains no information about.

## Reproducing

```bash
pip install -e ".[all]"
make bench        # writes benchmarks/{train,dev,test}.jsonl and benchmarks/stats.json
```

`scripts/build_bench.py` is the only path from the two dictionaries to these rows,
and it asserts its own `hard` property rather than trusting it. Nothing is pushed
anywhere: `--push` exists, defaults off, and refuses to publish on its own.

## Citation

```
Downstep: a compound pitch-accent benchmark with a dictionary-ablation
condition for words the TTS dictionary does not have.
Derived from UniDic 2.1.2 (The UniDic Consortium / NINJAL, BSD-3 option)
and NAIST-jdic (Nara Institute of Science and Technology, BSD-3).
No human annotation.
```

---

*Substantiates: **unlisted-word accuracy** — these rows plus the ablation condition are
what "the dictionary does not have this compound" is measured on; and **reproducibility**,
since `make bench` is the only path from the two dictionaries to them.*

	---
	language:
	- ja
	license: bsd-3-clause
	license_link: https://opensource.org/license/bsd-3-clause
	pretty_name: Downstep compound pitch-accent benchmark (listed / unlisted / hard)
	task_categories:
	- token-classification
	tags:
	- japanese
	- pitch-accent
	- prosody
	- tts
	- openjtalk
	- compounds
	- generalization
	- data-splits
	- multi-label
	annotations_creators:
	- machine-generated
	language_creators:
	- expert-generated
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: train.jsonl
	- split: dev
	path: dev.jsonl
	- split: test
	path: test.jsonl
	---

	# Downstep compound pitch-accent benchmark

	**Japanese noun compounds, their mora segmentation, and every accent their source
	dictionaries attest. Built so that "the model has never seen this compound" is a
	condition you can actually turn on, rather than a claim you have to trust.**

	> No human annotation is present in this release. Every accent label here is
	> dictionary-derived. `docs/ANNOTATION_GUIDELINES.md` ships the protocol, the CSV
	> format and the agreement statistics for collecting native-speaker judgements on
	> novel compounds — but that collection has not happened, and nothing in this
	> repository should be read as if it had. There are no annotator IDs, no agreement
	> scores and no human-adjudicated items.

	---

	## Sources and licences

	Two independently produced lexicons. Downstep reads the copies installed by
	`unidic-lite` and `pyopenjtalk` and never fetches anything over the network.

	\| Source \| Holder \| Licence \| Reached via \|
	\|---\|---\|---\|---\|
	\| UniDic 2.1.2 (unidic-mecab) \| The UniDic Consortium / NINJAL \| GPL / LGPL / BSD 3-Clause, tri-licensed — Downstep relies on the BSD option \| `unidic-lite` \|
	\| NAIST-jdic (Open JTalk's system dictionary) \| Nara Institute of Science and Technology \| BSD 3-Clause \| `pyopenjtalk` \|

	Attribution both licences require, reproduced in full in [`NOTICE`](../NOTICE) and
	required of anyone redistributing these rows:

	- Retain the copyright notice, the list of BSD 3-Clause conditions and the warranty
	disclaimer.
	- Do not use "The UniDic Consortium", "NINJAL", "Nara Institute of Science and
	Technology" or their contributors to endorse or promote anything derived from this
	data without prior written permission. Downstep makes no such endorsement claim.

	The BSD option is what makes the derived rows redistributable at all: under the GPL
	option a redistributed derivative would have to be GPL, which is why the choice is
	stated explicitly rather than left to the reader.

	The tooling in this repository is Apache-2.0. The data rows below are governed by
	BSD 3-Clause through their sources.

	### What is redistributed, and what is not

	Redistributed: derived rows — surface, reading, mora segmentation, the set of
	attested accents, the component decomposition, and the split/stratum labels.

	Not redistributed: the dictionaries themselves, in compiled or CSV form; any
	audio; any human judgement. `downstep.lexicon.load_lexicons()` rebuilds the local
	cache (`data/lexicon/*.jsonl`, git-ignored) from the installed packages in ~20 s.

	Measured in the cached dump on 2026-09-03 (`make lexicon`, then a count over the
	JSONL):

	\| \| UniDic 2.1.2 \| NAIST-jdic \|
	\|---\|---:\|---:\|
	\| entries \| 751,705 \| 788,331 \|
	\| distinct surfaces \| 568,123 \| 461,975 \|
	\| noun entries \| 277,189 \| 455,743 \|
	\| distinct noun surfaces with an accent \| 258,371 \| 418,671 \|
	\| noun entries recording more than one accent \| 21,568 \| 0 \|
	\| noun surfaces whose accents disagree across entries \| 24,844 \| 5,854 \|

	Those two right-hand columns are the raw material for the multiple-answer field.
	NAIST-jdic records one accent per entry, so its variation only appears when two
	entries share a surface; UniDic records variation inside a single entry
	(`"0,4,3,1"`, CSV-quoted — a naive `split(",")` both mis-counts the fields and
	destroys exactly these rows).

	---

	## What one row is

	A noun compound that satisfies both of:

	1. It is a lexicalised entry in NAIST-jdic, so a lexicographer assigned it an accent.
	That is the gold.
	2. It decomposes into two or more morphemes whose readings compose back to the
	compound's own reading, with 連濁 / 促音化 accounted for. That is what makes it a
	composition problem rather than a lookup.

	Cut points for (2) are constrained to a morphological analyser's boundaries. The
	analyser is UniDic while the gold is NAIST-jdic — deliberately a different
	dictionary, so the decomposition is not read off the answer. Without that constraint
	the decomposition collapses: a naive dynamic program over dictionary surfaces splits
	ファインマン into ファ+イン+マ+ン, because nearly every single kana is itself an entry,
	and reading-consistency alone cannot reject it.

	## Fields

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `surface` \| str \| The compound \|
	\| `reading` \| str \| Katakana reading, devoicing marks and interpuncts removed \|
	\| `moras` \| list[str] \| Mora segmentation; `len(moras) == n_moras` is enforced at build time \|
	\| `n_moras` \| int \| Mora count \|
	\| `gold_accents` \| list[int] \| A set, not a value. `0` = 平板; `k` = nucleus on mora `k`, 1-indexed \|
	\| `has_variation` \| bool \| `len(gold_accents) > 1` — the dictionaries attest more than one accent \|
	\| `components` \| list[obj] \| Per component: `surface`, `reading`, `accent`, `n_moras`, `goshu`, `acon_type`, `in_lexicon` \|
	\| `n_components` \| int \| Number of components (always ≥ 2) \|
	\| `goshu` \| str \| 語種 of the compound, taken as the modal component value (和 / 漢 / 外 / 固 / 混) \|
	\| `script` \| str \| `kanji` / `katakana` / `mixed` \|
	\| `is_proper` \| bool \| The NAIST entry's POS is 固有名詞 \|
	\| `all_components_in_lexicon` \| bool \| Every component was found in a source lexicon \|
	\| `split` \| str \| `train` / `dev` / `test` \|
	\| `hard` \| bool \| Test-only: no component of this compound occurs in any training compound \|
	\| `source` \| str \| `naist-jdic`, or `naist-jdic+unidic` when the accent set draws on both \|

	### `gold_accents` is the multiple-accepted-answers field

	Japanese pitch accent genuinely admits more than one correct answer for many words,
	and the dictionaries record that. Scoring against a single "true" accent would
	manufacture errors out of real variation. So:

	- Score membership, not equality: a prediction is correct if it is in
	`gold_accents`. `downstep.metrics.nucleus_exact_match` takes a set.
	- `has_variation` marks these rows so they can be reported as their own stratum,
	because a system that always guesses the majority variant looks better on them than
	it deserves.
	- This field records lexicographic variation between and within dictionaries. It
	is not, and must not be presented as, a survey of how speakers pronounce these
	words.

	---

	## Splits, conditions and strata — three different things

	Splits partition the rows. Assignment is `sha1(surface)` thresholded, not a
	shuffled seed, so adding new candidate words never moves an existing word between
	splits and any machine reproduces the same partition.

	\| split \| share \| rows \|
	\|---\|---\|---\|
	\| `train` \| 79.9% \| 71,544 \|
	\| `dev` \| 10.0% (calibration and abstention threshold) \| 8,959 \|
	\| `test` \| 10.1% \| 9,036 \|

	Total: 89,539 compounds.

	Conditions are not splits. `listed` and `unlisted` are the same test rows
	evaluated twice, with the dictionary's whole-word entry present and then ablated at
	query time. See [`docs/METHODOLOGY.md`](../docs/METHODOLOGY.md) for why the ablation
	must be rebuilt per target — batching corrupted 47.7% of targets in the pilot.

	`hard` is a partition of the test set. Two nested flags:

	* `hard` (818 rows, 9.0% of test) — the
	compound's final morpheme never occurs in any training compound. The final element
	(後部要素) governs compound accent in the classical rules, so this is the linguistically
	meaningful generalisation test.
	* `hard_all` (105 rows) — no component occurs in training. Stricter,
	but too small to carry a claim; kept as a stratum.

	Reported separately, never averaged into the headline: a pilot found a memorisation-shaped
	model gained nothing on unseen final components (58.3% vs 58.3%). The trained model does
	better than that — but the split exists so that can be checked rather than assumed.

	\| stratum field \| why it is a stratum \|
	\|---\|---\|
	\| `is_proper` \| Proper nouns are a different problem with different conventions; they are 66% of these rows (59,430 of 89,539) and are reported on their own, never folded into a single number \|
	\| `goshu` \| 和語 / 漢語 / 外来語 compounds follow different accent regularities \|
	\| `n_components` \| Two-part compounds are much easier than three-part ones \|
	\| `n_moras` \| Longer compounds have more positions to get wrong \|
	\| `has_variation` \| Rows with more than one accepted answer \|
	\| `script` \| Katakana (largely loanword) compounds behave unlike kanji ones \|

	Row counts, from `benchmarks/stats.json`:

	\| stratum \| counts \|
	\|---\|---\|
	\| script \| kanji 55,237 · mixed 27,290 · katakana 7,012 \|
	\| 語種 (`goshu`) \| 和 34,760 · 固 25,085 · 漢 23,313 · 外 4,443 · 混 604 · 記号 570 · unknown 764 \|
	\| components \| 2 → 70,419 · 3 → 16,286 · 4 → 2,539 · 5+ → 295 \|
	\| moras \| 4 → 21,228 · 5 → 19,522 · 6 → 18,884 · 7 → 11,112 · 8+ → 18,793 \|
	\| noun type \| proper 59,430 · common 30,109 \|
	\| more than one attested accent \| 1,030 \|
	\| every component in a lexicon \| 89,539 (all of them — decomposition requires it) \|

	---

	## Limitations, stated plainly

	- The gold is a dictionary's opinion. Dictionaries disagree with each other and
	with speakers. Where they disagree with each other the row keeps both answers; where
	they disagree with speakers, this data cannot tell.
	- The variety recorded is a broadly Tokyo-based standard. That is what these two
	lexicons encode, so it is a scoping decision forced by the available data. It is
	not a claim that other regional or generational accent systems are incorrect, and
	a Downstep prediction is not a correction of anyone's speech.
	- These are all words that exist in a dictionary. The target application is
	compounds that do not. The ablation device is what bridges that gap, and its limits
	are argued — not waved away — in `docs/METHODOLOGY.md`.
	- Reading-consistent decomposition rejects real compounds. The rendaku model covers
	連濁 and 促音化 but not 連声 or stem alternations (雨 → あま, 酒 → さか), so compounds
	built on those are dropped rather than mis-segmented. That is a coverage limitation
	the numbers do not show.
	- No speech. No F0 contours, no durations, no speaker information. Accent here is
	an integer, not a measurement.
	- Not a pronunciation dictionary. Do not use these rows as a reference for how a
	word "should" be said. For that, consult NHK日本語発音アクセント新辞典 or
	明解日本語アクセント辞典.

	## Out-of-scope uses

	- As an authority on correct pronunciation, in education or assessment.
	- To evaluate or rank speakers, or to flag any pronunciation as an error.
	- As evidence about regional variation, which it contains no information about.

	## Reproducing

	```bash
	pip install -e ".[all]"
	make bench # writes benchmarks/{train,dev,test}.jsonl and benchmarks/stats.json
	```

	`scripts/build_bench.py` is the only path from the two dictionaries to these rows,
	and it asserts its own `hard` property rather than trusting it. Nothing is pushed
	anywhere: `--push` exists, defaults off, and refuses to publish on its own.

	## Citation

	```
	Downstep: a compound pitch-accent benchmark with a dictionary-ablation
	condition for words the TTS dictionary does not have.
	Derived from UniDic 2.1.2 (The UniDic Consortium / NINJAL, BSD-3 option)
	and NAIST-jdic (Nara Institute of Science and Technology, BSD-3).
	No human annotation.
	```

	---

	Substantiates: unlisted-word accuracy* — these rows plus the ablation condition are
	what "the dictionary does not have this compound" is measured on; and reproducibility,
	since `make bench` is the only path from the two dictionaries to them.*