--- license: cc-by-nc-sa-4.0 language: - en - de - fr task_categories: - text-generation tags: - morphology - tokenization - inflection - agreement - babylm pretty_name: MorphBench Verb Cloze (EN/DE/FR) configs: - config_name: de data_files: - split: train path: de/train-* - split: dev path: de/dev-* - split: test path: de/test-* - split: test_lemhigh_formlow path: de/test_lemhigh_formlow-* - split: test_lemhigh_formhigh path: de/test_lemhigh_formhigh-* - split: test_lemlow_formlow path: de/test_lemlow_formlow-* - split: test_lemlow_formhigh path: de/test_lemlow_formhigh-* - config_name: en data_files: - split: train path: en/train-* - split: dev path: en/dev-* - split: test path: en/test-* - split: test_lemhigh_formlow path: en/test_lemhigh_formlow-* - split: test_lemhigh_formhigh path: en/test_lemhigh_formhigh-* - split: test_lemlow_formlow path: en/test_lemlow_formlow-* - split: test_lemlow_formhigh path: en/test_lemlow_formhigh-* - config_name: fr data_files: - split: train path: fr/train-* - split: dev path: fr/dev-* - split: test path: fr/test-* - split: test_lemhigh_formlow path: fr/test_lemhigh_formlow-* - split: test_lemhigh_formhigh path: fr/test_lemhigh_formhigh-* - split: test_lemlow_formlow path: fr/test_lemlow_formlow-* - split: test_lemlow_formhigh path: fr/test_lemlow_formhigh-* dataset_info: - config_name: de features: - name: prompt dtype: string - name: gold dtype: string - name: lemma dtype: string - name: context dtype: string - name: feats dtype: string - name: phenomenon dtype: string - name: cell dtype: string - name: resource dtype: string - name: verbclass dtype: string - name: source_treebank dtype: string - name: person_number dtype: string - name: form_freq dtype: int64 - name: lemma_other_freq dtype: int64 - name: form_level dtype: string - name: lemma_level dtype: string splits: - name: train num_bytes: 2354707 num_examples: 6000 - name: dev num_bytes: 390682 num_examples: 1000 - name: test num_bytes: 1089247 num_examples: 2740 - name: test_lemhigh_formlow num_bytes: 333756 num_examples: 824 - name: test_lemhigh_formhigh num_bytes: 312192 num_examples: 800 - name: test_lemlow_formlow num_bytes: 346472 num_examples: 868 - name: test_lemlow_formhigh num_bytes: 96827 num_examples: 248 download_size: 2231070 dataset_size: 4923883 - config_name: en features: - name: prompt dtype: string - name: gold dtype: string - name: lemma dtype: string - name: context dtype: string - name: feats dtype: string - name: phenomenon dtype: string - name: cell dtype: string - name: resource dtype: string - name: verbclass dtype: string - name: source_treebank dtype: string - name: person_number dtype: string - name: form_freq dtype: int64 - name: lemma_other_freq dtype: int64 - name: form_level dtype: string - name: lemma_level dtype: string splits: - name: train num_bytes: 1227501 num_examples: 3601 - name: dev num_bytes: 157395 num_examples: 468 - name: test num_bytes: 362180 num_examples: 1051 - name: test_lemhigh_formlow num_bytes: 30733 num_examples: 92 - name: test_lemhigh_formhigh num_bytes: 276722 num_examples: 800 - name: test_lemlow_formlow num_bytes: 27036 num_examples: 75 - name: test_lemlow_formhigh num_bytes: 27689 num_examples: 84 download_size: 933032 dataset_size: 2109256 - config_name: fr features: - name: prompt dtype: string - name: gold dtype: string - name: lemma dtype: string - name: context dtype: string - name: feats dtype: string - name: phenomenon dtype: string - name: cell dtype: string - name: resource dtype: string - name: verbclass dtype: string - name: source_treebank dtype: string - name: person_number dtype: string - name: form_freq dtype: int64 - name: lemma_other_freq dtype: int64 - name: form_level dtype: string - name: lemma_level dtype: string splits: - name: train num_bytes: 1261637 num_examples: 3542 - name: dev num_bytes: 176931 num_examples: 493 - name: test num_bytes: 401435 num_examples: 1119 - name: test_lemhigh_formlow num_bytes: 94431 num_examples: 259 - name: test_lemhigh_formhigh num_bytes: 285562 num_examples: 800 - name: test_lemlow_formlow num_bytes: 20551 num_examples: 57 - name: test_lemlow_formhigh num_bytes: 891 num_examples: 3 download_size: 968834 dataset_size: 2241438 --- # MorphBench — contextual verb-inflection cloze (EN / DE / FR) A verb in a natural sentence is replaced by the marker `[x]`. The prompt gives the lemma and a **partial** feature bundle; the sentence supplies exactly the missing dimension. The model generates the surface form. ``` prompt : cloze lemma= context= feats= -> gold : the inflected surface form ``` The benchmark exists to test whether a **morphology-aware tokenizer** helps a small LM inflect words it has seen little of. That question is only answerable if the items are stratified by how much the model actually saw during pre-training, which is what the 2x2 `cell` field is for. ## Configs and splits ```python from datasets import load_dataset ds = load_dataset("yuanxin112/morphbench-verb-cloze", "de") # "en" | "de" | "fr" ds["test_lemhigh_formlow"] # the headline cell ``` | split | EN | DE | FR | |---|---|---|---| | `train` | 3601 | 6000 | 3542 | | `dev` | 468 | 1000 | 493 | | `test` | 1051 | 2740 | 1119 | | `test_lemhigh_formlow` | 92 | 824 | 259 | | `test_lemhigh_formhigh` | 800 | 800 | 800 | | `test_lemlow_formlow` | 75 | 868 | 57 | | `test_lemlow_formhigh` | 84 | 248 | 3 | `test` is the union of the four cell splits; use whichever is convenient. ## What is withheld, and what the sentence has to supply | config | phenomenon | `feats` gives | withheld | cue in the sentence | |---|---|---|---|---| | de | `agree_pres` | `v;ind;prs` | person + number | the subject (`nsubj`) | | de | `agree_past` | `v;ind;pst` | person + number | the subject | | de | `ptcp` | `v;ptcp` | — (participle *formation*) | an auxiliary licenses the reading | | fr | `agree_pres` | `V;IND;PRS` | person + number | the subject | | fr | `agree_imp` | `V;IND;IPFV;PST` | person + number | the subject | | fr | `agree_ps` | `V;IND;PFV;PST` | person + number | the subject | | fr | `agree_fut` | `V;IND;FUT` | person + number | the subject | | fr | `agree_cnd` | `V;COND` | person + number | the subject | | fr | `agree_sbjv` | `V;SBJV;PRS` | person + number | the subject | | en | `agree_pres` | `V;IND;PRS` | person + number | the subject | | en | `past` | `V;IND[;P;N]` | **tense** | another past finite verb in the sentence | | en | `ptcp_vs_past` | `V;PST` | **VerbForm** | an auxiliary (`have`/`be`) | Two of these are not on the person/number axis. `en/ptcp_vs_past` withholds **VerbForm**. The feats string is identical for both answers; only the sentence decides: ``` The revolt [x] two weeks to be suppressed. -> took (no auxiliary) the custom group fields are [x] . -> hidden (auxiliary) ``` It is restricted to the UniMorph lemmas whose participle actually differs from their past; for regular verbs the two are syncretic (`walked`/`walked`) and there would be nothing to decide. `de/ptcp` is participle **formation** — the ge-circumfix (`ge-macht`, `ge-fahren`), its absence on -ieren verbs (`studiert`) and inseparable prefixes (`verstanden`), and the infix on separable ones (`auf-ge-kommen`). Only tokens whose auxiliary is present are taken, so the sentence licenses the participle reading. German does **not** get the two-way version: its finite half would be a `machte` vs `machten` person/number choice that feats cannot state without leaking the answer, so it would compete with `agree_past` for the same tokens and strip it of every 3sg item. English has no such conflict because `walked` is person/number-invariant. English needs the second axis. Outside `be` (excluded as an auxiliary) no English verb varies its past form by person or number — `walked` fills all six slots — so withholding person+number there would leave the subject carrying no information at all. German and French have no such problem (their past cells average 4.0 and 5.1–5.8 distinct forms over the six person/number slots in UniMorph), which is why they use one axis throughout. Per config: - **en** — `agree_pres` 527, `past` 462, `ptcp_vs_past` 62 - **de** — `agree_pres` 1052, `agree_past` 888, `ptcp` 800 - **fr** — `agree_pres` 550, `agree_imp` 196, `agree_ps` 154, `agree_fut` 118, `agree_cnd` 63, `agree_sbjv` 38 ## Frequency stratification (`cell`) Two axes, each split low/high at **1 occurrence per million words of that language's pre-training corpus** (~11 occurrences in these ~10M-word corpora): - `lemma_other_freq` — how often the model saw this verb through its **other** forms (the UniMorph paradigm total *minus* the target form). Excluding the target is what keeps the two axes independent; a paradigm total that included it would make `form_high` imply `lemma_high` and collapse the grid into a triangle. - `form_freq` — how often it saw this exact surface string. | cell | reading | |---|---| | `lem_high+form_low` | the verb is familiar, this form is not — **where a morphological tokenizer should win** | | `lem_high+form_high` | both familiar — memorisation ceiling | | `lem_low+form_low` | nothing to go on — floor | | `lem_low+form_high` | the form is common but the rest of the paradigm is not — a frozen/lexicalised form | A plain seen/unseen split was tried first and rejected: on a 10M-word corpus 93–97% of items land in seen+seen and the interesting cell holds 7–46 rows. `lem_high+form_high` is capped at 800 rows per language (it is only a ceiling estimate); the other three cells are taken in full, and rare lemmas are deliberately routed to `test` rather than `train` by the split. ## Fields | field | meaning | |---|---| | `prompt`, `gold` | what the model reads / must produce | | `lemma`, `context`, `feats` | the prompt, pre-parsed | | `phenomenon` | see the table above | | `cell` | 2x2 frequency stratum | | `form_freq`, `lemma_other_freq` | raw counts in the pre-training corpus | | `form_level`, `lemma_level` | `low` / `high`, the two axes of `cell` | | `person_number` | the withheld value (`3sg`, `3pl`, …; `-` where UD leaves English past unmarked) | | `verbclass` | EN `s_reg`/`s_ortho`/`bare`/`regular`/`irregular`/`ptcp`/`pret`; DE `strong`/`weak`/`ptcp`; FR `g1`/`g1_stem`/`g2`/`irregular` | | `resource` | whether the form/lemma is in that language's UniMorph | | `source_treebank` | UD treebank the sentence came from — **needed to filter by licence, see below** | ## Construction guarantees - **Lemma-disjoint** train / dev / test. - **Sentence-disjoint** too. One UD sentence yields one item per finite verb, and those items used to land in different splits, leaving the test answer verbatim in the training copy of the same sentence (35.6% of English test rows before this was fixed). Verified 0 shared sentences. - **No hand-written cue word lists.** An earlier build recovered the tense from an adverb list containing *then / once / back / last / soon / later*; 8.3% of English test items had only such an adverb as their cue, i.e. the present tense was equally grammatical and the item had no unique answer. - **Capped per (phenomenon, lemma)** so accuracy is not an average over ~20 frequent verbs, and **per (phenomenon, person-number) within each cell** so a model cannot score well by always emitting the 3sg form. - **Blank marker `[x]`** occurs 0 times in all three pre-training corpora and in no source treebank, and is split into exactly 3 pieces by every tokenizer tested in all three languages — so the marker itself gives no tokenizer a shorter prompt. ## Pre-training corpora the frequencies refer to | config | corpus | tokens | |---|---|---| | en | `babylm_strict_small.txt` (BabyLM-style ~10M) | ~10M | | de | `babylm_deu_10m.txt` (BabyLM-style ~10M) | ~10M | | fr | `babylm_fra_10m.txt` (BabyLM-style ~10M) | ~10M | The counts are only meaningful for a model pre-trained on those corpora. They are **not** cleaned of CHAT speaker stubs (`*TARGET_CHILD:` etc.), which make up 4.7% / 9.7% / 7.7% of the EN / DE / FR corpora — the models saw those tokens, so removing them here would misstate exposure. ## Licence and attribution **CC BY-NC-SA 4.0.** The sentences are derived from Universal Dependencies treebanks, several of which are CC BY-NC-SA; ShareAlike propagates, so the whole dataset carries the most restrictive licence in the mix. `source_treebank` is on every row, so a user who needs a permissively licensed subset can filter to it: ```python permissive = {"ewt", "atis", "pud", "gsd", "hdt", "rhapsodie", "parisstories"} ds = ds.filter(lambda r: r["source_treebank"] in permissive) ``` | config | treebank | licence | rows in `test` | |---|---|---|---| | en | UD_ewt | CC BY-SA 4.0 | 421 | | en | UD_gum | CC BY-NC-SA 4.0 **(NC)** | 337 | | en | UD_lines | CC BY-NC-SA 4.0 **(NC)** | 202 | | en | UD_partut | CC BY-NC-SA 4.0 **(NC)** | 34 | | en | UD_pud | CC BY-SA 3.0 | 28 | | en | UD_gentle | CC BY-NC-SA 4.0 **(NC)** | 20 | | en | UD_atis | CC BY-SA 4.0 | 9 | | de | UD_hdt | CC BY-SA 4.0 | 2171 | | de | UD_gsd | CC BY-SA 4.0 | 569 | | fr | UD_gsd | CC BY-SA 4.0 | 712 | | fr | UD_rhapsodie | CC BY-SA 4.0 | 105 | | fr | UD_sequoia | LGPL-LR | 89 | | fr | UD_parisstories | CC BY-SA 4.0 | 86 | | fr | UD_pud | CC BY-SA 3.0 | 59 | | fr | UD_partut | CC BY-NC-SA 4.0 **(NC)** | 39 | | fr | UD_fqb | LGPL-LR | 29 | ## Known limitations - French `lem_low+form_high` has almost no items (the whole of French UD holds ~20). This is a fact about French, not a sampling failure: a frequent form there almost always belongs to a frequent paradigm. Do not report that cell for French. - English `lem_high+form_low` and `lem_low+form_low` are small (English paradigms have ~4 forms, so a frequent verb's forms are all frequent). - Non-3sg present equals the bare lemma in English and German, so a model that blindly copies the lemma scores a non-trivial floor there (EN ~27%, DE ~23%, FR 0%). Compare configs with that in mind. - Scoring should be **case-insensitive**: a handful of golds come from all-caps or headline text. ## Citation Please cite Universal Dependencies and the individual treebanks listed above.