Phonebook — reading Japanese corporate names by structure, not by memory

Getting the reading of "株式会社◯◯" right is easy. The hard part is the ◯◯ you have never seen.

A character-level seq2seq model specialised in mapping Japanese corporate names to katakana readings. About 17M parameters, built to run on CPU, with a copy mechanism, constrained decoding, calibrated confidence and a rejection option.

Code, dataset builder, evaluation and figures: https://github.com/NagaYu/phonebook

⚠️ This checkpoint is trained on synthetic data

The weights published here were trained on the synthetic corpus produced by scripts/make_synthetic.py, which exists so the full pipeline can be validated without the National Tax Agency bulk download. They are a working reference implementation, not a production model. For real performance, fetch the NTA data and retrain — the repository automates every step. The evaluation numbers shipped with the repository carry the same warning.

Usage

from phonebook import load_reader
reader = load_reader("path/to/model")
result = reader.read("株式会社日本電気", nbest=3)
print(result.reading, result.confidence)   # reading and calibrated confidence
print(result.to_dict()["candidates"])      # n-best with probabilities

phonebook read "株式会社日本電気" --nbest 3

Design

1. StructuralSplitter — strips the legal form (leading/trailing, parentheses, shop names) with rules and assigns it a deterministic reading, so the model only sees the trade-name core. This is not only about speed: it removes from the evaluation the effect where memorizing カブシキガイシャ inflates apparent accuracy.
2. CharSeq2Seq with a copy mechanism — a character-level Transformer. The pointer-generator lets it transcribe katakana and hiragana already present in the input. Kana-only spans are transcribed deterministically, so copying is a structural guarantee rather than a learned habit.
3. Constrained decoding — the vocabulary is restricted to full-width katakana, the prolonged mark and EOS. The output character set is therefore a property of the search space, not of training, and cannot be violated even by untrained weights.
4. Calibration and rejection — Platt scaling turns the score into a probability, and below a threshold the model answers "unknown".

Evaluation

Read the numbers per split. An overall average is dominated by the abundance of known entities and invites mistaking memorization for generalization.

Conditions: (A) pyopenjtalk / MeCab+UniDic, (B) a large LLM with prompting only, (C) Phonebook, (D) Phonebook after Q4_K_M quantization. Metrics: exact match (strict and long-vowel-normalized), CER, n-best@3 coverage, ECE, coverage and precision under rejection, ms/item and resident RSS on CPU.

Measured results (synthetic corpus - see the warning above)

Exact match with long vowels normalized, which is the fair comparison against G2P systems that emit a pronunciation form:

Character error rate: Phonebook 0.004 / 0.027 / 0.027 / 0.120 against pyopenjtalk 0.144 / 0.140 / 0.143 / 0.185. CPU: ~54 ms/item at beam 8 without a KV cache.

Where the hypothesis fails, stated plainly. The design hypothesis was that the gap would widen on unseen entities. It does not. Phonebook leads pyopenjtalk by +0.415 on known entities but only +0.115 on unseen and +0.121 on hard, and its own drop from known to hard is -0.31 against pyopenjtalk's -0.01. It remains clearly ahead in absolute terms on unseen entities (0.649 vs 0.533, with roughly five times lower CER), but "ahead" and "the gap widens" are different claims and only the first one holds here.

Calibration behaves the same way: ECE is 0.019 on known entities, 0.274 on unseen and 0.786 on ambiguous. The rejection threshold fitted on dev (0.366) leaves coverage at 0.99 on the test splits and barely lifts precision, because dev is dominated by known-entity-like cases. Fit the threshold on data matching your deployment distribution.

Quantization is free: Q4_K_M matches fp32 within +/-0.003 exact match on every split, at 10.8 MB of weights and the same latency.

Full tables: benchmarks/RESULTS.md in the repository.

Export formats (note)

• GGUF (Q4_K_M / Q8_0): real Q4_K / Q6_K / Q8_0 block structure, bit-exact round-trip. But llama.cpp cannot run these files — Phonebook is a custom architecture. GGUF is used for distribution and inspection.
• ONNX: runs as-is under onnxruntime; beam search runs on the host.
• MLX: weights (npz) plus config; see mlx/README.md for a loading example.

Limitations

• The target is corporate names. Reading personal names is not the objective and is kept to a LoRA transfer experiment.
• Same-surface/different-reading cases (日本 = ニホン / ニッポン) have no unique answer in principle. Use the n-best and the confidence rather than top-1 exact match. On the ambiguous set the model is also poorly calibrated: it is confident about the majority reading, which is precisely the wrong one there.
• Latin script is resolved by lexicon → acronym → Hepburn romaji → English spelling rules. English spelling-to-sound is rule-based and imperfect.
• Output is an estimate, not official furigana.

Training data

Source: National Tax Agency Corporate Number Publication Site (https://www.houjin-bangou.nta.go.jp/) — created by processing that data. The published information may be used under terms conforming to the Japanese Government's Public Data License (Version 1.0), which requires crediting the source and stating that the data has been modified. Check the current terms at https://www.houjin-bangou.nta.go.jp/pc/riyokiyaku/.

(The checkpoint published here is trained on synthetic data modelled on that schema; see the warning above.)

Model weights are distributed under Apache-2.0.