# MASSIVE Russian warm-start for the routing heads

Measured 2026-08-08 on workpc (Radeon RX 7900 GRE, ROCm). Covers V-546 step 2.
Workspace is `~/Programs/embed-training` on workpc, scripts `train_massive.py`,
`ab_run.py`, `ab.sh`, `probe_time.py`.

## What was trained

Two heads on a copy of multilingual-e5-small: `Linear(384, 60)` for MASSIVE's
own intents over a masked mean pool, `Linear(384, 111)` per token for BIO slot
tags. MASSIVE's label sets verbatim, no alignment to Maven's 7 intents. The
intent head is an auxiliary loss that shapes the pooled vector and is thrown
away.

Data is `amazon-massive-dataset-1.1` pulled from S3. The Hugging Face repo is
script-only and `datasets` 5.0 refuses those, so `load_dataset` cannot fetch it.
`ru-RU` is 11,514 train, 2,033 dev, 2,974 test, 60 intents, 55 slots, 111 BIO
labels. All 16,521 rows survived span alignment: `annot_utt` re-tokenised to its
own `utt` on every one.

Hyperparameters match `train_intent.py`, so the two runs differ in data only.
Frozen XLM-R vocabulary, body 2e-5, heads 1e-3, batch 32, sequence 64, 10
epochs. MASSIVE's own dev partition selects the epoch, on slot F1 with intent
accuracy as tiebreak. Selecting on 60-class intent accuracy would optimise a
head that gets deleted.

## Result

Epoch 9 of 10 by dev slot F1. Held-out MASSIVE test: intent 86.2%, slot span
F1 71.5% (P 68.5, R 74.8). Peak 1.70GB of 17.2GB, about 22 seconds an epoch,
under 4 minutes end to end. Dev slot F1 climbed monotonically to epoch 9 and
fell at 10, so 10 epochs was the right budget.

Ten slot types sit at 0% test recall. Every one of them has 1 to 7 test
instances: `alarm_type` has 3, `drink_type` has 1. That is support in MASSIVE's
Russian split, not a tagger failure. `playlist_name` at 6% of 16 is the first
real miss.

## The intent A/B, and why it settles nothing

`train_intent.py` was run against both bodies, three seeds by two smoothing
settings, on `train_v4.jsonl`. It is v4 and not v5 because v4 is what
`sweep2.log` measured. `ab_run.py` strips a `--base` flag onto the module global, so
`train_intent.py` is unmodified and its baseline stays reproducible. The stock
arm reproduced `sweep2.log` line for line.

Fixture accuracy, 91 cases, one case is 1.1 points:

| seed / smooth | stock | warm-started |
|---|---|---|
| 0 / 0.0 | 94.0% | 92.8% |
| 0 / 0.1 | 95.2% | 92.8% |
| 1 / 0.0 | 95.2% | 94.0% |
| 1 / 0.1 | 95.2% | 97.6% |
| 2 / 0.0 | 92.8% | 94.0% |
| 2 / 0.1 | 92.8% | 96.4% |

Mean 94.2% against 94.6%. That is +0.4 points, about a third of one case, and
inside seed noise. Spread widened. Stock lands in a 2.4-point band and
warm-started in a 4.8-point one. The warm-started arm holds both the best result
of the sweep and a tie for the worst. Seed 0 is the bad arm and it fails in a
specific way. Its dev peaks at epoch 2 and 3 and never improves, where stock
peaks around 7. The dev slice is a quarter of the seed rows. That is small
enough that early stopping is fragile when the body arrives already fitted.

**The A/B was never the test.** Intent had at most 4.8 points of headroom here.
MASSIVE was not trained for Maven's intents. Read it as "the warm-start does not
cost intent accuracy", nothing more.

## The measurement that does mean something

`want_time` is the one slot Maven's fixture scores, and MASSIVE has `time` and
`date`. Restricted to those two slot types, F1 is 74.9% over 609 gold spans on the
MASSIVE ru test split. Precision is 71.5 and recall 78.7. That beats the 71.5%
all-slot figure. Of the 530 test utterances carrying a time or a date, 73.4% get
every such span exactly right.

Out of domain matters more, because Maven's traffic is not this corpus. Ten
Maven-shaped utterances, none of them in MASSIVE:

| utterance | tagged |
|---|---|
| `напомни в 11:00 позвонить маме` | `time='11:00'`, `relation='маме'` |
| `напомни завтра в семь утра выпить таблетки` | `date='завтра'`, `time='семь утра'` |
| `поставь будильник на полседьмого` | `time='полседьмого'` |
| `через двадцать минут напомни про чайник` | `time='двадцать минут'` |
| `напомни в пятницу вечером забрать посылку` | `date='пятницу'`, `timeofday='вечером'` |
| `что у меня сегодня после обеда` | `date='сегодня'`, `time='после'`, `timeofday='обеда'` |
| `запиши что кофе закончился` | nothing |
| `что такое TCP` | `definition_word='TCP'` |

The first row is the V-572 defect utterance. `ReminderGrammar` handed the daemon
`HasTime: false` there, and the daemon asked "Когда?" at a sentence that had
already said when. `полседьмого` is a colloquial half-past that no digit pattern
catches. `запиши что кофе закончился` correctly carries nothing, because a note
has no time.

Two errors. `после обеда` split into `time='после'` plus `timeofday='обеда'`
when it is one span, and `через двадцать минут` dropped its `через`. Both are
boundary errors on spans the tagger did find.

Unplanned: `что такое TCP` returned `definition_word='TCP'`. MASSIVE has a slot
for the thing being asked about, which is a `SourceWorld` signal sitting in a
head already trained.

Ten hand-picked utterances are evidence, not a fixture.

## What this does not measure

Maven has no span fixture. `want_time` and `want_fn` are presence booleans and
`want_fact_key` is an exact string match, so nothing in the repo can score a
71.5% span tagger. Destination got one the same day, at 12/33 on the classifier
cascade: see `2026-08-08-destination-fixture.md`.

The missing span fixture is why the warm-start stays unjudged against Maven
rather than against MASSIVE.

## Datasets ruled out

Checked on 2026-08-08 and rejected as label sources:

- **MASSIVE's other 50 locales** ship in the same tarball and are parallel by id.
  Co-training on them is free and unmeasured. English was ruled out by the owner
  on 2026-08-08.
- **CLINC150** is reachable as parquet, 150 intents and 1,200 explicit
  out-of-scope queries, English only. Its value is the labeled out-of-scope set
  for fitting the energy threshold, not intent labels.
- **`d0rj/dolphin-ru`**, roughly 2.8M rows of FLAN-style tasks translated to
  Russian. No intent, no slots, and not utterances anyone says to an assistant.
- **`psytechlab/EmpatheticIntents-ru`**, 24,856 rows of translated
  EmpatheticDialogues with 32 emotion labels. Maven's mood enum is `neutral,
  happy, thinking, tired, confused` and it describes her own reply, not the
  speaker's emotion. No mapping exists.
- **`ai-forever/MERA`** and **`RussianNLP/russian_super_glue`**, benchmark
  harnesses. Rows are prompt templates with `{toxic_comment}` placeholders.
- **`ZeroAgency/ru-big-russian-dataset`**, an LLM-judge quality corpus. Its
  `question` and `classified_topic` columns are a usable Russian out-of-scope
  pool for threshold fitting. That is the one thing CLINC150 can only supply in
  English. The questions are long and written, so they belong in the negative
  set, never in the in-scope `query` training set.
- No second Russian slot-filling corpus exists. The xSID mirrors are 404,
  MultiATIS++ has no Russian, SLURP is not on the Hub.