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 ---
+pretty_name: WMT26 MT-Eval
+task_categories:
+- translation
+language:
+- arb
+- arz
+- bel
+- ces
+- deu
+- ekk
+- ell
+- eng
+- hin
+- hye
+- ind
+- isl
+- jpn
+- kaz
+- kor
+- lij
+- lld
+- rus
+- sme
+- tha
+- ukr
+- vie
+- zho
+size_categories:
+- 10M<n<100M
+tags:
+- machine-translation
+- mt-evaluation
+- quality-estimation
+- wmt
 configs:
-- config_name: default
+- config_name: items
   data_files:
   - split: evaluation
-    path: "wmt26-grand.parquet"
+    path: data/items/evaluation-*.parquet
+- config_name: pairs
+  default: true
+  data_files:
+  - split: evaluation
+    path: data/pairs/evaluation-*.parquet
+- config_name: submissions
+  data_files:
+  - split: evaluation
+    path: data/submissions/evaluation-*.parquet
+- config_name: baselines
+  data_files:
+  - split: evaluation
+    path: data/baselines/evaluation-*.parquet
+- config_name: systems
+  data_files:
+  - split: evaluation
+    path: data/systems/evaluation-*.parquet
+- config_name: mt_systems
+  data_files:
+  - split: evaluation
+    path: data/mt_systems/evaluation-*.parquet
+- config_name: challenge_sets
+  data_files:
+  - split: evaluation
+    path: data/challenge_sets/evaluation-*.parquet
+- config_name: grand
+  data_files:
+  - split: evaluation
+    path: wmt26-grand.parquet
 ---
 
-# WMT26 grand evaluation dataset
+# WMT26 MT-Eval
+
+Test data and evaluator predictions of the **WMT26 Shared Task on Automated Translation Quality
+Evaluation Systems**, the shared task on MT metrics and quality estimation (findings paper: Lavie
+et al., 2026; see [Citation](#citation)).
+
+Participants built automatic evaluators of translation quality. The organisers gave them one test
+set, made of source texts and candidate translations taken from the WMT26 General Machine
+Translation task (General MT), and asked for contributions of four kinds. Tasks 1 to 3 asked for
+predictions; Task 4 asked for challenge sets:
+
+| task | what it asks for | columns |
+| --- | --- | --- |
+| Task 1 | error spans with a severity, and omissions | `t1_errors`, `t1_omission` |
+| Task 2 | a quality score for each translation, from 0 to 100 | `t2_score` |
+| Task 3 | whether a translation is free of errors | `t3_error_free` |
+| Task 4 | challenge sets that probe the evaluators: five were submitted by research teams, and the two RefQuality sets are the organisers' own | `challenge_sets`, `challenge_set` |
+
+The test set covers 21 official language pairs, 8 of them
+priority pairs that the organisers recommended to participants who could not run the whole set,
+and 7 challenge sets. Submissions to one task were also converted
+automatically to the downstream tasks in the findings paper; those conversions are not part of
+v0.9.
+
+This repository holds the test set, every candidate translation, and the predictions of every
+participant run and every organiser baseline, as typed Parquet tables that load in one line.
+
+- **26,885 items** in `items`: 8,518 official items in 21 language pairs and 18,367 challenge items.
+- **363,242 pairs** in `pairs`: 252,923 official and 110,319 challenge.
+- **Empty or whitespace-only hypotheses: 3,785** (3,689 exactly empty, 96 whitespace-only).
+- **18,021 pairs are reference candidates**: the hypothesis equals the reference, up to surrounding whitespace.
+- **11,297,384 rows** in `submissions` and **10,753,092 rows** in `baselines`.
+- **73 evaluator runs** in `systems`, **55 MT systems** in `mt_systems` and **7 challenge sets** in `challenge_sets`.
+
+## What this release contains
+
+This is **v0.9**: typed data configs and documentation.
+
+- It contains the seven typed configs below and the organisers' original single file as `grand`.
+- v0.9 contains no human-annotation gold in its typed configs. The human evaluation was carried
+  out by General MT and is public in
+  [its release](https://github.com/wmt-conference/wmt26-general-mt/releases/tag/humeval).
+  A gold config is planned for v1.0, together with the converted predictions, the results tables
+  and the scoring code.
+  The unchanged `grand` config still carries the organisers' raw annotation records in its
+  `ground_truth__record` column; see [Human annotations](#human-annotations).
+
+## Quick start
+
+Every config has one split, named `evaluation`: this is one evaluation set, not partitioned into
+train/test sets. The typed configs load with `datasets>=2.18`:
+
+```python
+from datasets import load_dataset
+
+pairs = load_dataset("wmt/wmt26-mteval-data", "pairs", split="evaluation")
+print(pairs[0]["lp"], pairs[0]["src"], pairs[0]["hyp"], sep="\n")
+```
+
+`pairs` is the default config. `submissions` and `baselines` are large (11,297,384
+and 10,753,092 rows), so you may prefer to stream them or to download one config:
+
+```python
+subs = load_dataset("wmt/wmt26-mteval-data", "submissions", split="evaluation", streaming=True)
+
+base = load_dataset("wmt/wmt26-mteval-data", "baselines", split="evaluation", streaming=True)
+# `baselines` also holds the original, pre-deduplication snapshot (`alt`); see Warnings
+base = base.filter(lambda row: row["variant"] == "official")
+```
+
+```bash
+# scripts/download.py is in the GitHub repository, https://github.com/wmt-conference/wmt26-mteval
+uv run python scripts/download.py --config submissions --out data/hf
+```
+
+The Parquet files can also be queried in place, for example with DuckDB:
+
+```python
+import duckdb
+
+base = "hf://datasets/wmt/wmt26-mteval-data/data"
+duckdb.sql(f"""
+    SELECT lp, system, count(*) AS n, sum(hyp_is_empty::int) AS n_empty
+    FROM '{base}/pairs/*.parquet'
+    WHERE collection = 'official'
+    GROUP BY lp, system ORDER BY lp, system
+""").show()
+duckdb.sql(f"SELECT run_id, name, tasks, n_items FROM '{base}/systems/*.parquet'").show()
+```
+
+The `grand` config is the organisers' single file with JSON columns. The `datasets` library reads
+those as dicts from version 4.7.0 on (`pip install "datasets>=4.7.0"`); older versions cannot
+load this config, but they load the seven typed configs:
+
+```python
+grand = load_dataset("wmt/wmt26-mteval-data", "grand", split="evaluation")
+```
+
+## Configs
+
+| config | rows | one row per | contents |
+| --- | ---: | --- | --- |
+| `items` | 26,885 | test item | Source text, reference and its type, the columns parsed from `item_id`, the paths of the media files and the number of systems that translated the item. |
+| `pairs` | 363,242 | item and MT system | The hypotheses: source, hypothesis, reference, whether the hypothesis is a copy of the reference (`is_reference_candidate`) and whether it is empty (`hyp_is_empty`). No gold columns in v0.9. |
+| `submissions` | 11,297,384 | participant run, item and system | What each participant's evaluator predicted: error spans, omission, score or error-free judgement, for the task it was entered in. |
+| `baselines` | 10,753,092 | organiser baseline run, item and system | The organisers' baselines in the same columns as `submissions`, plus `variant` (`official` or `alt`). |
+| `systems` | 73 | official evaluator run | The run's name, organisation, curated paper name and citation, allow-listed answers from its model card, and what it covers. |
+| `mt_systems` | 55 | MT system | Every system that has hypotheses in `pairs`, with its type, items and language pairs. |
+| `challenge_sets` | 7 | challenge set | Authors, citation, what the set tests, what its hypotheses and references are, and its counts. |
+| `grand` | 26,885 | test item | The organisers' lossless archive, unchanged: item fields, the raw annotation records and one JSON column per submission and baseline snapshot (needs `datasets>=4.7.0`). |
+
+- Join the hypotheses to the predictions on `(item_id, system)`, then join the predictions to
+  `systems` on `run_id`; for `baselines`, filter `variant == 'official'` first (see Warnings).
+- `baselines.variant` is `official` for the released predictions: the deduplicated snapshot of
+  the LLM baselines, the xCOMET baselines (whose files are identical before and after
+  deduplication) and the segment-level metric baselines, which have a single snapshot. `alt` is
+  the original, not deduplicated snapshot of the LLM baselines, with Task 1 columns only. An
+  earlier snapshot, which the organisers label `pre_dedup_pre_enhy_fix`, is in the `grand` config
+  only. The `official` label is provisional: Section 4.2 of the findings paper describes
+  deduplication as part of how the baselines were made, so the deduplicated snapshot is `official`
+  for now. It will be checked against the paper in v1.0. Only the results computed from the
+  error spans can tell the two snapshots apart (Tables 4-7 and 16-36 of the findings paper), and
+  Table 6, which needs no human annotation, already fits the deduplicated snapshot: its errors per
+  prediction for the Qwen 3.6 baselines match the deduplicated predictions, not the original ones.
+- A run may cover only some items, and then has no rows for the others; `systems.n_items`, `lps`
+  and `covers_all_priority` say what it covers.
+- `systems` holds allow-listed model-card answers only: a fixed list of fields was copied, and
+  nothing else was read. The links in `link` were volunteered by the teams for publication.
+  `name` is the name the team gave its run (followed by the run id where several runs share one
+  name), and `canonical_name` is the name the findings paper uses.
+- `t1_instruction_fault` is null in every row of this release.
+- The baseline diagnostic `stage1_finish_reason`, which some baseline runs record, appears only
+  in the `grand` config.
+
+## How to read the data
+
+### Item identifiers
+
+An `item_id` has six fields joined by the separator `_###_`:
+
+```text
+<collection>_###_<source>_###_<target>_###_<domain or set>_###_<document>_###_<para>
+official_###_eng_Latn_###_arz_Arab_###_news_###_brisbanetimes.com.au.377415_###_0
+```
+
+| field | column | meaning |
+| --- | --- | --- |
+| 1 | `collection` | `official` for the main test set, `challenge` for a challenge set. |
+| 2, 3 | `src_lang_orig`, `tgt_lang_orig` | The language codes as written in the id; `src_lang` and `tgt_lang` hold the normalised codes. |
+| 4 | `domain` or `challenge_set` | The domain of an official item (`news`, `social`, `speech`, `software`, `edu` or `factchecking`), or the name of the challenge set (`GAMBITP`, `FECE`, `QEbreak`, `AGGREE` or `LIGATUR`). The two RefQuality sets keep the domain here and carry their name in the document field. |
+| 5 | `doc_id` | The source document. The document of a RefQuality item starts with the name of its set (`RefQuality-PD_` or `RefQuality-PE_`); one that starts with `RefQuality-PD_RefQuality-PE_` marks the pseudo-reference variant of an item whose alternative reference is a post-edit. |
+| 6 | `para_idx` | The zero-based paragraph index (see below). |
+
+`para_idx` is null for FECE and QEbreak, whose ids end in a segment hash instead of a paragraph number.
+
+Split on the whole separator, not on `#`: `item_id.split("_###_")`. Document ids can themselves contain `_`, `|` and `.`.
+
+### Language codes
+
+Codes are normalised to an ISO 639-3 language code and an ISO 15924 script, plus a region where one is needed (`eng_Latn`, `zho_Hans`, `zho_Hant_TW`); the original codes stay in `src_lang_orig` and `tgt_lang_orig`. The `lp` column is the paper's short form: the first three letters of each side, except that a Simplified Chinese target is `zho_CN` and Traditional Chinese (Taiwan) is `zho_TW` (`eng-zho_CN`, `eng-zho_TW`, `zho-jpn`).
+
+### Reference types
+
+`ref_type` says how the reference was produced:
+
+| `ref_type` | source data | meaning |
+| --- | --- | --- |
+| `native` | `human` | A human translation from scratch. |
+| `postedit` | `postedit` | A post-edited machine translation. |
+| `pseudo` | `pseudo` | A machine-generated pseudo-reference. |
+| `unknown` | `undefined` | A reference text is present, but the source data does not record its type. |
+| `none` | absent, or null text and type | The item has no reference text, and `ref_text` is null. |
+
+`items.ref_type_orig` keeps the type exactly as the source data spells it (`human`, `postedit`, `pseudo`, `undefined`, or null where none is recorded).
+
+### System types
+
+`system_type` classifies the system behind a hypothesis:
+
+| `system_type` | the `system` is |
+| --- | --- |
+| `mt` | A machine translation system. |
+| `human_scratch` | `Human (from scratch)`. |
+| `human_postedit` | `Human (postediting)`. |
+| `pseudo_ref` | `PseudoRef`, the pseudo-reference. |
+| `challenge_hyp` | A challenge-set hypothesis such as `FECE_hyp1`. |
+
+### Missing values
+
+Every column may be null; the description of each column says when. A run that does not cover an item has no rows for it (there are no null rows), and a run that makes no prediction for a task has null in that task's columns. Text values are copied exactly from the source data: nothing is trimmed, re-cased or clipped.
+
+## Warnings
+
+- **`baselines` holds two snapshots of some runs.** 12 runs have an `alt`
+  snapshot next to their `official` one, and its 3,469,756 `alt` rows share their
+  `run_id` with the official rows. Counting or averaging `baselines` by `run_id`, or joining it to
+  `systems` on `run_id`, therefore counts those runs twice. Filter `variant == 'official'` unless
+  you want the pre-deduplication snapshot.
+- **References are among the candidate translations of official items.** In the official set,
+  every available reference is also a hypothesis (`system_type` is `human_scratch`,
+  `human_postedit` or `pseudo_ref`). Challenge hypotheses can equal the reference too:
+  `challenge_hyp` rows can be `is_reference_candidate = true`. In all, 18,021
+  pairs have a hypothesis that equals the item's reference, up to surrounding whitespace, and
+  carry `is_reference_candidate = true`. A reference-based evaluator is given the reference as
+  well, so it sees a copy of it on those rows.
+
+  `is_reference_candidate` also flags MT outputs that are identical to the reference, so it does
+  not mark the reference systems only. By `system_type`, the flagged pairs are:
+
+    - `mt`: 8,673 (MT outputs that equal the reference)
+    - `human_scratch`: 2,888 (`Human (from scratch)`, a reference system)
+    - `human_postedit`: 1,714 (`Human (postediting)`, a reference system)
+    - `pseudo_ref`: 3,627 (`PseudoRef`, a reference system)
+    - `challenge_hyp`: 1,119 (challenge hypotheses that equal the reference)
+
+  To drop the reference itself, filter on `system_type`: remove the flagged rows of
+  `human_scratch`, `human_postedit` and `pseudo_ref`. Do not drop every `is_reference_candidate`
+  row: that removes real MT outputs and challenge hypotheses as well.
+- **The GPT-5.6 Sol baseline at xHigh reasoning is incomplete.** The findings paper says this
+  run completed only 6 of the 8 priority pairs. In the data, its `t2_score` is the same
+  placeholder value, 50.0, in every row, and its Task 1 predictions are empty for almost all of
+  `eng-sme` (no row has an error span) and for most of `eng-ind`. Do not use its `t2_score`, and
+  treat its `eng-sme` and `eng-ind` predictions as incomplete.
+- **Some hypotheses are empty.** There are 3,785 empty or whitespace-only
+  hypotheses (3,689 exactly empty and 96
+  whitespace-only); `hyp_is_empty` is true for all of them. They are kept, and so are the
+  predictions made for them.
+- **RefQuality items repeat sources of official items.** The two RefQuality challenge sets
+  (`RefQuality-PD` and `RefQuality-PE`) re-use the sources of official items with a different
+  reference, a pseudo-reference or a post-edit, to show what the reference type changes. Their
+  ids differ from the official ones, so filter on `collection` before you count sources. A
+  `doc_id` that starts with `RefQuality-PD_RefQuality-PE_` marks the pseudo-reference variant of
+  an item whose alternative reference is a post-edit.
+- **Three language pairs have little human annotation.** The share of pairs that General MT
+  annotated is only 10.6 % for `ces-vie`, 17.1 % for `eng-zho_CN` and 19.0 % for `eng-sme`
+  (findings paper, Table 2), so meta-evaluation on them rests on few judgements.
+- **Items are paragraph-level.** The last field of `item_id` is a paragraph index, and the
+  "segments" of the findings paper are these items. A hypothesis can span several sentences, and
+  the `start` and `end` of a Task 1 span are character offsets into that whole hypothesis.
+- **`Cohere CAT+` is two things.** It is an MT system that has hypotheses in `pairs` and an
+  evaluator that has a row in `systems`. Do not join `pairs.system` to `systems.name`: in the
+  predictions, `run_id` names the evaluator and `system` the translation it judged.
+- **Task 2 scores can fall outside 0-100.** Some runs predict below 0 or above 100, and some
+  wrote integers; every score is stored unchanged as `float64`.
+- **The challenge sets differ in their references.** GAMBITP has no reference. AGGREE, FECE,
+  LIGATUR and QEbreak have a reference translation, but the source data does not record its type
+  (`ref_type` is `unknown`) and the findings paper does not say how it was produced. The
+  RefQuality sets have pseudo-references or post-edits.
+- **Challenge sets are not scored like the official set.** Their authors analysed the results
+  with their own code. Which of the two hypotheses of a challenge item is which is not stated in
+  the findings paper for several sets; see `challenge_sets.hyp_semantics`.
+- **`t1_instruction_fault` is null in every row**, and `para_idx` is null for FECE and QEbreak
+  items.
+
+## Coverage of the official test set
+
+One line per official language pair: its source items, its pairs (candidate translations), the
+type of its reference and whether it is a priority pair.
+
+| language pair | source items | pairs | reference | priority |
+| --- | ---: | ---: | --- | --- |
+| `ces-deu` | 438 | 13,578 | native | yes |
+| `ces-ukr` | 415 | 12,035 | none | no |
+| `ces-vie` | 418 | 12,958 | native | yes |
+| `eng-arz` | 433 | 12,990 | postedit | yes |
+| `eng-bel` | 354 | 10,974 | pseudo | no |
+| `eng-ces` | 448 | 13,440 | postedit | no |
+| `eng-deu` | 459 | 13,770 | postedit | no |
+| `eng-ekk` | 356 | 10,680 | native | yes |
+| `eng-hye` | 364 | 10,192 | pseudo | no |
+| `eng-ind` | 331 | 10,261 | pseudo | yes |
+| `eng-isl` | 360 | 11,160 | native | yes |
+| `eng-kaz` | 438 | 12,702 | pseudo | no |
+| `eng-lij` | 411 | 11,919 | native | no |
+| `eng-lld` | 376 | 10,152 | native | no |
+| `eng-rus` | 426 | 13,206 | pseudo | no |
+| `eng-sme` | 493 | 13,311 | native | yes |
+| `eng-tha` | 451 | 13,981 | pseudo | no |
+| `eng-ukr` | 406 | 12,180 | pseudo | no |
+| `eng-zho_CN` | 481 | 13,949 | pseudo | no |
+| `eng-zho_TW` | 325 | 9,100 | pseudo | no |
+| `zho-jpn` | 335 | 10,385 | postedit | yes |
+| **all official** | **8,518** | **252,923** | | |
+
+The counts are those of the `items` and `pairs` configs; the release checks them against Table 2
+of the findings paper. `native` is the "Human" reference of that table. The challenge sets are not
+in this table: they hold 18,367 items and 110,319 pairs.
+
+## Field glossary
+
+The same tables are in
+[`docs/FORMAT.md`](https://github.com/wmt-conference/wmt26-mteval/blob/main/docs/FORMAT.md),
+and the descriptions are stored in the schema of every Parquet file.
+
+### `items`
+
+One row per test item.
+
+| column | type | description |
+| --- | --- | --- |
+| `item_id` | `string` | Unique identifier of the test item: six fields (collection, source language, target language, domain or challenge-set name, document, paragraph or segment) joined by `_###_`. |
+| `collection` | `string` | Either `official` for the main test set or `challenge` for a challenge set. |
+| `challenge_set` | `string` | Name of the challenge set the item belongs to (`GAMBITP`, `FECE`, `QEbreak`, `AGGREE`, `LIGATUR`, `RefQuality-PD` or `RefQuality-PE`), or null for official items. |
+| `domain` | `string` | Text domain (`news`, `social`, `speech`, `software`, `edu` or `factchecking`) for official and RefQuality items, or null for every other challenge item. |
+| `src_lang` | `string` | Source language, normalised to a three-letter language code, a script and, where needed, a region (for example `eng_Latn` or `zho_Hant_TW`). |
+| `tgt_lang` | `string` | Target language, normalised to a three-letter language code, a script and, where needed, a region (for example `deu_Latn` or `zho_Hans`). |
+| `src_lang_orig` | `string` | Source language code exactly as it is written in `item_id` (for example `en` or `eng_Latn`). |
+| `tgt_lang_orig` | `string` | Target language code exactly as it is written in `item_id` (for example `de_DE` or `deu_Latn`). |
+| `lp` | `string` | Language pair as a short `src-tgt` code, such as `ces-deu` or `eng-zho_CN`. |
+| `is_priority` | `bool` | True for official items whose language pair is one of the eight priority directions, and false for every other item, challenge items included. |
+| `doc_id` | `string` | Identifier of the source document the item belongs to, the fifth field of `item_id`. |
+| `para_idx` | `int32` | Zero-based index of the paragraph within its document, taken from the last field of `item_id`, and null for FECE and QEbreak, whose item ids end in a segment hash. |
+| `src` | `string` | Source text of the item, exactly as in the test set. |
+| `ref_text` | `string` | Reference translation, exactly as in the test set, or null when the item has no reference. |
+| `ref_type` | `string` | How the reference was produced: `native` (human translation from scratch), `postedit` (post-edited machine translation), `pseudo` (machine-generated pseudo-reference), `unknown` (a reference text is present but its type is not recorded) or `none` (no reference text). |
+| `ref_type_orig` | `string` | Reference type exactly as recorded in the source data (`human`, `postedit`, `pseudo` or `undefined`), or null when the data records no type. |
+| `asr` | `string` | Automatic speech recognition transcript of the source audio, present for speech items only. |
+| `video_path` | `string` | Relative path of the source video file as recorded in the test set, present for speech items only. |
+| `screenshot_path` | `string` | Relative path of the screenshot of the original post, present for some social media items only. |
+| `n_systems` | `int32` | Number of systems (machine translation, human translation, pseudo-reference or challenge hypothesis) that have a hypothesis for the item. |
+
+### `pairs`
+
+One row per item and MT system.
+
+| column | type | description |
+| --- | --- | --- |
+| `item_id` | `string` | Unique identifier of the test item: six fields (collection, source language, target language, domain or challenge-set name, document, paragraph or segment) joined by `_###_`. |
+| `collection` | `string` | Either `official` for the main test set or `challenge` for a challenge set. |
+| `challenge_set` | `string` | Name of the challenge set the item belongs to (`GAMBITP`, `FECE`, `QEbreak`, `AGGREE`, `LIGATUR`, `RefQuality-PD` or `RefQuality-PE`), or null for official items. |
+| `domain` | `string` | Text domain (`news`, `social`, `speech`, `software`, `edu` or `factchecking`) for official and RefQuality items, or null for every other challenge item. |
+| `src_lang` | `string` | Source language, normalised to a three-letter language code, a script and, where needed, a region (for example `eng_Latn` or `zho_Hant_TW`). |
+| `tgt_lang` | `string` | Target language, normalised to a three-letter language code, a script and, where needed, a region (for example `deu_Latn` or `zho_Hans`). |
+| `src_lang_orig` | `string` | Source language code exactly as it is written in `item_id` (for example `en` or `eng_Latn`). |
+| `tgt_lang_orig` | `string` | Target language code exactly as it is written in `item_id` (for example `de_DE` or `deu_Latn`). |
+| `lp` | `string` | Language pair as a short `src-tgt` code, such as `ces-deu` or `eng-zho_CN`. |
+| `is_priority` | `bool` | True for official items whose language pair is one of the eight priority directions, and false for every other item, challenge items included. |
+| `doc_id` | `string` | Identifier of the source document the item belongs to, the fifth field of `item_id`. |
+| `para_idx` | `int32` | Zero-based index of the paragraph within its document, taken from the last field of `item_id`, and null for FECE and QEbreak, whose item ids end in a segment hash. |
+| `system` | `string` | Name of the system that produced the hypothesis, exactly as in the source data. |
+| `system_type` | `string` | Kind of system: `mt`, `human_scratch`, `human_postedit`, `pseudo_ref` or `challenge_hyp`. |
+| `src` | `string` | Source text of the item, exactly as in the test set. |
+| `hyp` | `string` | Hypothesis translation, exactly as in the source data, including empty and whitespace-only outputs. |
+| `ref_text` | `string` | Reference translation, exactly as in the test set, or null when the item has no reference. |
+| `ref_type` | `string` | How the reference was produced: `native` (human translation from scratch), `postedit` (post-edited machine translation), `pseudo` (machine-generated pseudo-reference), `unknown` (a reference text is present but its type is not recorded) or `none` (no reference text). |
+| `is_reference_candidate` | `bool` | True when the hypothesis equals the reference apart from surrounding whitespace, so that it may be a copy of the reference; it marks the reference itself (a `human_scratch`, `human_postedit` or `pseudo_ref` row) and also any MT output or challenge hypothesis that equals it, so filter on `system_type` to tell them apart. |
+| `hyp_is_empty` | `bool` | True when the hypothesis is empty or contains only whitespace. |
+
+### `submissions`
+
+One row per participant run, item and system.
+
+| column | type | description |
+| --- | --- | --- |
+| `run_id` | `string` | Identifier of the evaluator run: the numeric submission id, or the name of the organisers' baseline (for example `gemma_ref` or `872013`). |
+| `task` | `string` | Task the run was entered for: `task1` (error spans, with Task 2 scores as well for the organisers' Task 1 baselines), `task2` (segment scores) or `task3` (error-free judgements). |
+| `item_id` | `string` | Unique identifier of the test item: six fields (collection, source language, target language, domain or challenge-set name, document, paragraph or segment) joined by `_###_`. |
+| `lp` | `string` | Language pair as a short `src-tgt` code, such as `ces-deu` or `eng-zho_CN`. |
+| `system` | `string` | Name of the system that produced the hypothesis, exactly as in the source data. |
+| `t1_errors` | `list<struct<start: int32, end: int32, severity: string, category: string>>` | Task 1 error spans the run predicts for the translation, each with `start` and `end` offsets into the hypothesis, a `severity` and a `category` (which may be null), or null where the run makes no Task 1 prediction for the row. |
+| `t1_omission` | `string` | Omission severity (`minor` or `major`) that the run predicts for the translation, or null when it predicts no omission or makes no Task 1 prediction. |
+| `t1_instruction_fault` | `string` | Instruction fault that the run reports for the translation, or null when it reports none or makes no Task 1 prediction (null in every row of this release). |
+| `t2_score` | `float64` | Task 2 quality score the run predicts for the translation, copied unchanged from the source (some runs score below 0 or above 100), or null where the run makes no Task 2 prediction. |
+| `t3_error_free` | `bool` | Task 3 judgement that the translation is free of errors, or null where the run makes no Task 3 prediction. |
+
+### `baselines`
+
+One row per organiser baseline run, item and system.
+
+| column | type | description |
+| --- | --- | --- |
+| `run_id` | `string` | Identifier of the evaluator run: the numeric submission id, or the name of the organisers' baseline (for example `gemma_ref` or `872013`). |
+| `task` | `string` | Task the run was entered for: `task1` (error spans, with Task 2 scores as well for the organisers' Task 1 baselines), `task2` (segment scores) or `task3` (error-free judgements). |
+| `item_id` | `string` | Unique identifier of the test item: six fields (collection, source language, target language, domain or challenge-set name, document, paragraph or segment) joined by `_###_`. |
+| `lp` | `string` | Language pair as a short `src-tgt` code, such as `ces-deu` or `eng-zho_CN`. |
+| `system` | `string` | Name of the system that produced the hypothesis, exactly as in the source data. |
+| `t1_errors` | `list<struct<start: int32, end: int32, severity: string, category: string>>` | Task 1 error spans the run predicts for the translation, each with `start` and `end` offsets into the hypothesis, a `severity` and a `category` (which may be null), or null where the run makes no Task 1 prediction for the row. |
+| `t1_omission` | `string` | Omission severity (`minor` or `major`) that the run predicts for the translation, or null when it predicts no omission or makes no Task 1 prediction. |
+| `t1_instruction_fault` | `string` | Instruction fault that the run reports for the translation, or null when it reports none or makes no Task 1 prediction (null in every row of this release). |
+| `t2_score` | `float64` | Task 2 quality score the run predicts for the translation, copied unchanged from the source (some runs score below 0 or above 100), or null where the run makes no Task 2 prediction. |
+| `t3_error_free` | `bool` | Task 3 judgement that the translation is free of errors, or null where the run makes no Task 3 prediction. |
+| `variant` | `string` | Snapshot of the baseline: `official` for the released predictions, or `alt` for the original predictions from before deduplication, with Task 1 fields only; the label is provisional and will be checked against the paper in v1.0. |
+
+### `systems`
+
+One row per official evaluator run.
+
+| column | type | description |
+| --- | --- | --- |
+| `run_id` | `string` | Identifier of the evaluator run: the numeric submission id, or the name of the organisers' baseline (for example `gemma_ref` or `872013`). |
+| `name` | `string` | Name the team gave the submission in its model card, followed by the run id in brackets where several runs share that name, so that every name is unique. |
+| `canonical_name` | `string` | Name under which the findings paper lists the system, curated by the organisers and never suffixed with the run id. |
+| `org` | `string` | Organisation or organisations that entered the run, as written in the model card. |
+| `description` | `string` | Short free-text description of the system, copied from the model card. |
+| `reference_usage` | `string` | Free-text account of which kinds of reference translation the system uses and how, copied from the model card. |
+| `model_size` | `string` | Size of the model behind the system, as the team wrote it in the model card. |
+| `closed_model_name` | `string` | Name of the closed-source model the system calls, as written in the model card. |
+| `api_provider` | `string` | Provider or host of the API through which the closed-source model is called, as written in the model card. |
+| `link` | `string` | Link to the submitted system that the team volunteered for publication in its model card, or an empty string where it gave none. |
+| `underlying_model` | `string` | Model or models behind the system as the findings paper reports them, curated by the organisers (`—` for the metrics that use no model, and `not stated in the paper` where the paper gives none). |
+| `paper_citation` | `string` | Citation of the paper that describes the system, in the author-year form of the findings paper, or `[Citation missing]` where the findings paper has none. |
+| `is_baseline` | `bool` | True for the organisers' baselines and false for participant submissions. |
+| `uses_reference` | `bool` | True when the model card says that the system uses the provided reference translations, false when it says that it does not, and null when the answer is missing or is neither yes nor no. |
+| `ensemble` | `bool` | True when the model card says that the system is an ensemble, false when it says that it is not, and null when the answer is missing or is neither yes nor no. |
+| `fine_tuned` | `bool` | True when the model card says that the team fine-tuned its model, false when it says that it did not, and null when the answer is missing or is neither yes nor no. |
+| `open_weight` | `bool` | True when the model card says that the system is based on open-weight models, false when it says that it is not, and null when the answer is missing or is neither yes nor no. |
+| `open_source` | `bool` | True when the model card says that the submitted system is open-source, false when it says that it is not, and null when the answer is missing or is neither yes nor no. |
+| `threshold_inclusive` | `bool` | True when a Task 2 score equal to `t3_threshold` counts as error-free, false when only a higher score does, and null when the model card says neither or both. |
+| `allow_t1_to_t2` | `bool` | True when the team allows the organisers to convert its Task 1 output into a Task 2 prediction automatically, false when it does not, and null when the model card gives no readable answer. |
+| `allow_t1_to_t3` | `bool` | True when the team allows the organisers to convert its Task 1 output into a Task 3 prediction automatically, false when it does not, and null when the model card gives no readable answer. |
+| `allow_t2_to_t3` | `bool` | True when the team allows the organisers to convert its Task 2 output into a Task 3 prediction automatically, false when it does not, and null when the model card gives no readable answer. |
+| `covers_all_priority` | `bool` | True when the run has at least one row for every one of the eight priority language pairs among the official items. |
+| `t3_threshold` | `float64` | Task 2 score that separates the team's Task 2 predictions that are converted to error-free judgements from those that are not: a higher score counts as error-free, and a score equal to the threshold does so when `threshold_inclusive` is true. Null where the model card gives no numeric threshold. |
+| `tasks` | `list<string>` | Tasks the run has predictions for: the one task of a submission, `task1` and `task2` for the organisers' Task 1 baselines, and `task2` for the seven segment-level metric baselines. |
+| `lps` | `list<string>` | Language pairs, as short codes sorted alphabetically, in which the run has at least one row, challenge sets included. |
+| `n_items` | `int64` | Number of distinct test items for which the run has at least one row. |
+| `n_pairs` | `int64` | Number of rows (one per item and system) that the run has in its prediction table. |
+
+### `mt_systems`
+
+One row per MT system.
+
+| column | type | description |
+| --- | --- | --- |
+| `system` | `string` | Name of the system that produced the hypothesis, exactly as in the source data. |
+| `system_type` | `string` | Kind of system: `mt`, `human_scratch`, `human_postedit`, `pseudo_ref` or `challenge_hyp`. |
+| `n_items` | `int64` | Number of distinct test items for which the system has a hypothesis. |
+| `lps` | `list<string>` | Language pairs, as short codes sorted alphabetically, in which the system has hypotheses. |
+
+### `challenge_sets`
+
+One row per challenge set.
+
+| column | type | description |
+| --- | --- | --- |
+| `name` | `string` | Name of the challenge set, as in the `challenge_set` column of `items` and `pairs`. |
+| `full_name` | `string` | Title of the paper that introduced the challenge set, copied from the reference list of the findings paper, or a descriptive name for the organisers' reference-swap sets, which have no published title. |
+| `authors` | `string` | Authors of the challenge set, as in the reference list of the findings paper. |
+| `citation` | `string` | Citation of the paper that describes the challenge set, in the author-year form of the findings paper. |
+| `what_it_tests` | `string` | Short description of the phenomenon that the challenge set probes, from the findings paper. |
+| `hyp_semantics` | `string` | What the hypotheses of the set are, and which `system` names they appear under, or `not stated in the paper` where the paper does not say. |
+| `ref_semantics` | `string` | What the reference of the set is, or `not stated in the paper` where the paper does not say. |
+| `lps` | `list<string>` | Language pairs, as short codes sorted alphabetically, in which the set has items. |
+| `n_items` | `int64` | Number of distinct items in the challenge set. |
+| `n_pairs` | `int64` | Number of rows (one per item and system) that the challenge set has in `pairs`. |
+
+## Known issues and errata
+
+The findings paper and the data differ in a few places, such as the number of official pairs, the
+number of translations per source, the deduplication rule of the baselines and the item counts of
+two challenge sets. They are listed, with the figures on both sides, in
+[`docs/ERRATA.md`](https://github.com/wmt-conference/wmt26-mteval/blob/main/docs/ERRATA.md).
+The release process is in
+[`docs/RELEASE.md`](https://github.com/wmt-conference/wmt26-mteval/blob/main/docs/RELEASE.md).
+
+## Personal data
+
+- The model cards of the participants ask for contact details and for consent. `systems` is built
+  from a fixed allow-list of answers. The fields that hold the names and e-mail addresses of the
+  submitters and the answers about data collection are not on the list, and their answers were
+  never read. The allow-listed answers were screened too: an e-mail address inside an
+  allow-listed answer is replaced by `[email removed]`, and one allow-listed answer that named
+  individuals was replaced by the organisations they belong to. The only e-mail address in the
+  metadata is the organisers' mailing list, below. A release check looks for any other address
+  in `systems`, `challenge_sets`, this card and the build manifest.
+- The links in `systems.link` were volunteered by the teams, in the model card, for publication.
+- Source texts and translations are published as they are in the WMT26 General MT test set, and
+  are not changed or scanned. They may contain personal details that occur in the original
+  public texts, such as social-media posts.
+- Annotator ids in the raw annotation records of `grand` are pseudonyms.
+
+If you find personal data that should not be here, write to
+`wmt-qe-metrics-organizers@googlegroups.com`.
+
+## Human annotations
+
+The human evaluation (contrastive error span annotation, cESA) was carried out by the WMT26
+General MT task (Kocmi et al., 2026a; Zouhar et al., 2026a). Its data, including the raw
+annotation logs, are public in General MT's release:
+
+<https://github.com/wmt-conference/wmt26-general-mt/releases/tag/humeval>
+
+v0.9 contains no human-annotation gold in its typed configs; a gold config is planned for v1.0.
+The unchanged `grand` config still holds the organisers' raw annotation records in the column
+`ground_truth__record`.
+
+The speech items point to video files and some social-media items to screenshots, in
+`items.video_path` and `items.screenshot_path`. These paths are relative to General MT's
+multimodal assets, the archive `multimodal_inputs.zip` in the release linked above.
+
+## Provenance
+
+The typed configs are derived only from `wmt26-grand.parquet` (sha256 `a61675552cebb68fb8303cf08a23fbec31e5bb4e0bd26a1fdee1a9bfafc2fb7a`), the
+scrubbed model cards, the list of priority pairs, and the curated tables of names, language codes
+and challenge sets that follow the findings paper. `build_manifest.json` lists the sha256 and the
+rows of every Parquet file, and the GitHub repository lists the source files in
+`data/MANIFEST.tsv`. The code that builds and checks this repository is at
+<https://github.com/wmt-conference/wmt26-mteval>.
+
+## Citation
+
+If you use this data, cite the findings paper:
+
+```bibtex
+@inproceedings{lavie2026findings,
+  title = {Findings of the WMT26 Shared Task on Automated Translation Quality Evaluation Systems: Compact Open Models are Competitive Quality Evaluators},
+  author = {Lavie, Alon and
+            Hanneman, Greg and
+            Perrella, Stefano and
+            Ding, Shuoyang and
+            Avramidis, Eleftherios and
+            Proietti, Lorenzo and
+            Lo, Chi-kiu and
+            Shurtz, Ammon and
+            Zerva, Chrysoula and
+            Sindhujan, Archchana and
+            Zouhar, Vilém and
+            Kanojia, Diptesh and
+            Blain, Frédéric and
+            Thompson, Brian and
+            Filandrianos, Giorgos and
+            Menis Mastromichalakis, Orfeas and
+            Kocmi, Tom and
+            Gupta, Pranav},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation (WMT)},
+  year = {2026},
+  pages = {931--1030},
+  url = {https://www2.statmt.org/wmt26/pdf/2026.wmt-1.49.pdf}
+}
+```
+
+The data build on the following work, as the findings paper cites it.
+
+The WMT26 General MT task, which provided the source texts, the translations and the human
+evaluation:
+
+```bibtex
+@inproceedings{kocmi2026general,
+  title = {Findings of the WMT26 general machine translation shared task: Contrastive dynamic human evaluation at scale},
+  author = {Kocmi, Tom and Artemova, Ekaterina and Avramidis, Eleftherios and Bawden, Rachel and Bojar, Ondřej and Dukanov, Sergey and Dvorkovich, Anton and Fishel, Mark and Freitag, Markus and Frontull, Samuel and Gowda, Thamme and Grundkiewicz, Roman and Haddow, Barry and Kharevich, Stan and Koehn, Philipp and Li, Zheng and Maillard, Jean and Monz, Christof and Murauski, Alexander and others},
+  booktitle = {To appear in Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  publisher = {Association for Computational Linguistics}
+}
+```
+
+The contrastive error span annotation protocol (cESA) of the human evaluation:
+
+```bibtex
+@misc{zouhar2026cesa,
+  title = {Contrastive esa: Human evaluation of multiple translations at once},
+  author = {Zouhar, Vilém and Grundkiewicz, Roman and Rajaee, Sara and Riley, Parker and Popel, Martin and Bawden, Rachel and Koehn, Philipp and Carpuat, Marine and Kocmi, Tom},
+  year = {2026},
+  eprint = {2607.26640},
+  archiveprefix = {arXiv}
+}
+```
+
+The pipeline that built the pseudo-references:
+
+```bibtex
+@inproceedings{kanojia2026blind,
+  title = {In the blind: Building pseudo-references for MT evaluation},
+  author = {Kanojia, Diptesh and Lo, Chi-kiu and Sindhujan, Archchana and Larkin, Samuel and Hanneman, Greg and Lavie, Alon},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  publisher = {Association for Computational Linguistics}
+}
+```
+
+The challenge sets (`RefQuality-PD` and `RefQuality-PE` are the organisers' own, in Section 5.2.1
+of the findings paper):
+
+```bibtex
+@inproceedings{griesbeck2026aggree,
+  title = {AGGREE: An aggregation-grounded challenge set for segment-level MT metric evaluation},
+  author = {Griesbeck, Lena and Geierhos, Michaela},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  address = {Budapest, Hungary},
+  publisher = {Association for Computational Linguistics}
+}
+
+@inproceedings{ding2026fece,
+  title = {FECE: A challenge set for evaluating fine-grained translation error correction},
+  author = {Ding, Jie and Duan, Xiangyu},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  address = {Budapest, Hungary},
+  publisher = {Association for Computational Linguistics}
+}
+
+@inproceedings{menismastromichalakis2026gambit,
+  title = {Benchmarking gender bias in machine translation evaluation metrics across occupations},
+  author = {Menis Mastromichalakis, Orfeas and Filandrianos, Giorgos and Mohammed, Wafaa and Attanasio, Giuseppe and Zerva, Chrysoula},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  address = {Budapest, Hungary},
+  publisher = {Association for Computational Linguistics}
+}
+
+@inproceedings{mishra2026ligatur,
+  title = {Translation metrics cannot judge what their tokeniser deletes: LIGATUR and AEGIS submissions to the WMT26 shared task on automated translation quality evaluation},
+  author = {Mishra, Animesh and Sharma, Krishang and Khetarpaul, Sonia},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  address = {Budapest, Hungary},
+  publisher = {Association for Computational Linguistics}
+}
+
+@inproceedings{silchenko2026qebreak,
+  title = {QEbreak at WMT26: A preregistered audit of omission and addition asymmetry in machine translation quality metrics},
+  author = {Silchenko, Maksim},
+  booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
+  year = {2026},
+  address = {Budapest, Hungary},
+  publisher = {Association for Computational Linguistics}
+}
+```
 
-One row per benchmark item, with translations, human judgments, team submissions, baseline predictions, and source provenance. Nested payloads use Hugging Face JSON features.
+## Contact
 
-This is one evaluation split; it is not partitioned into train/test sets. Raw multi-item annotation sessions are archived separately and are outside this benchmark table.
+The organisers: `wmt-qe-metrics-organizers@googlegroups.com`. Web page of the shared task:
+<https://www2.statmt.org/wmt26/mteval-task.html>.
diff --git a/build_manifest.json b/build_manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..6cd9c86099d375769e560ff7b6c6142264801756
--- /dev/null
+++ b/build_manifest.json
@@ -0,0 +1,825 @@
+{
+  "configs": {
+    "baselines": {
+      "files": [
+        {
+          "path": "data/baselines/evaluation-872013-official.parquet",
+          "rows": 332895,
+          "sha256": "c1444fbc7821a8731aae2d8b4e799cfe41e065ba32a1441b6a64b1659943458b"
+        },
+        {
+          "path": "data/baselines/evaluation-872028-official.parquet",
+          "rows": 332895,
+          "sha256": "899321ba61e8d57b053a9403e990aef419ed186cb744d89515be61073006d45f"
+        },
+        {
+          "path": "data/baselines/evaluation-872029-official.parquet",
+          "rows": 332895,
+          "sha256": "98131179c098309e8937541a93c7872b87507c69acde7a668058a9be426e8a16"
+        },
+        {
+          "path": "data/baselines/evaluation-872030-official.parquet",
+          "rows": 332895,
+          "sha256": "6a8fda37a4791de74477d5b103da0ae0fe8dc8d25c402d6def219f91b37f38b4"
+        },
+        {
+          "path": "data/baselines/evaluation-872031-official.parquet",
+          "rows": 332895,
+          "sha256": "3c6a159fe0535e6f549029928fff102a91eb8edad94a689966cde2faedb67d0e"
+        },
+        {
+          "path": "data/baselines/evaluation-873001-official.parquet",
+          "rows": 332895,
+          "sha256": "60d1dfe289ccd645f8f470dccc93ab572e61d76722e390d1c9baded5248418f9"
+        },
+        {
+          "path": "data/baselines/evaluation-873002-official.parquet",
+          "rows": 363242,
+          "sha256": "c4a69193d334f1fcb723a32dc70d269d055df36a68781ab355d6035244cff942"
+        },
+        {
+          "path": "data/baselines/evaluation-gemini-alt.parquet",
+          "rows": 277851,
+          "sha256": "bb49311c6251a862cff71f5c8a5b292c49a709db5192db896a3f1ef4507a1d10"
+        },
+        {
+          "path": "data/baselines/evaluation-gemini-official.parquet",
+          "rows": 277851,
+          "sha256": "538df296d5c28aaf7a373d641da80c466ebb6867ba69c9fff9a7a1e82ede0c63"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma-alt.parquet",
+          "rows": 363242,
+          "sha256": "b2a5773707f864dd675b62006b5b333d4bdaf5cdb6195677a2e045c081cd5dbe"
+        },
+        {
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+          "rows": 363242,
+          "sha256": "6232f2d31c436ffd2cc5332d471c38ba18e8ba2f4191a8c79aadbdbd95f329e2"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma4_no_thinking-alt.parquet",
+          "rows": 363242,
+          "sha256": "644ffcb47944a43c8f103bc2b35e25eec9fff52d55c2e9b0a54e926e63490271"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma4_no_thinking-official.parquet",
+          "rows": 363242,
+          "sha256": "ce5e2b9243249b1d3a0368205583d0da65272bc349e55676026e073eb503047b"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma_ref-alt.parquet",
+          "rows": 363242,
+          "sha256": "906149c476d9c5f9499e9c2be5e757199f7f90084c8194a09a32f36a5f4097c2"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma_ref-official.parquet",
+          "rows": 363242,
+          "sha256": "8f1d139413c9ed06ce990cf58b1bde295fb12993c6d09866325d43437a51f7f7"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma_ref_no_thinking-alt.parquet",
+          "rows": 363242,
+          "sha256": "e1e20dad5b1a798d75edc5d25bedbac324a1753c103a80c84f1ac650720204a6"
+        },
+        {
+          "path": "data/baselines/evaluation-gemma_ref_no_thinking-official.parquet",
+          "rows": 363242,
+          "sha256": "1e415e35993c5d6f11ccc32f6a8f3798f2ddd2b98385995402c4c2be0405ed07"
+        },
+        {
+          "path": "data/baselines/evaluation-gpt56_sol_xhigh_no_ref-alt.parquet",
+          "rows": 95323,
+          "sha256": "826ee87d3749cf381ee7ca7e2b7e5d3c8039b96c94c978c51f337f15d4e82c63"
+        },
+        {
+          "path": "data/baselines/evaluation-gpt56_sol_xhigh_no_ref-official.parquet",
+          "rows": 95323,
+          "sha256": "99cf01f01380000ec0542ba051f8eda3276989c03dc68fe42470976985985df2"
+        },
+        {
+          "path": "data/baselines/evaluation-gpt56_terra_no_ref-alt.parquet",
+          "rows": 95323,
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