Lapsus

Lapsus is a small dataset of grammatical and ungrammatical sentences in English and Italian. It was made for the course Elaborazione del Linguaggio Naturale at Kore University of Enna, for hands-on exercises with BERT-style models. The code that builds it is on GitHub.

How it works

We take clean sentences from Tatoeba and break some of them on purpose, keeping track of what we broke and where. There are five kinds of errors:

• word_order: two nearby words are swapped ("i never your cut hair")
• missing_word: a function word is deleted ("she went the station")
• repeated_word: a word is doubled ("he is is tired")
• agreement: a form no longer agrees ("the dogs runs", "la ragazzi")
• wrong_function_word: the wrong preposition or article ("in" instead of "on", "di" instead of "da")

In every split 50% of the sentences are clean, 30% have one error and 20% have two or three (labelled multiple).

Each broken sentence is checked before it is kept: the new word must be a real form, the result must not be another clean sentence, and a small language model must like it less than the original, that is $\log p(\text{clean}) - \log p(\text{corrupted}) \geq \delta$ (average per token). The checks are automatic, so a few mistakes are still in there.

Tasks

The same rows support four tasks. For "the dogs runs fast":

task	column	label
binary	binary	not_ok
error type	multiclass	agreement
error types (multi-label)	multilabel	$[0, 0, 0, 1, 0]$
token tagging	token_tags	runs is tagged agreement, the rest O

The multilabel order is word_order, missing_word, repeated_word, agreement, wrong_function_word. When a word is missing, the token right after the gap gets the tag.

Configs and splits

config	train	validation	test	test_unseen_lemmas
en	11865	1483	1483	1500
it	12000	1500	1500	1500
both	23865	2983	2983	3000
parallel	120	20	6	4

both is en plus it with a lang column. parallel is a small set of English-Italian translation pairs (pair_id). In test_unseen_lemmas the content words never appear in the other splits, so it tests generalization rather than memorization.

from datasets import load_dataset

ds = load_dataset("morenolq/lapsus", "it")

Other useful columns: tokens, clean_text (the original sentence), n_corruptions, source_id, source_author and license. Text is lowercased and the final punctuation is removed; Italian elisions are split (l'uomo becomes l' + uomo).

Licence

The dataset is released as cc-by-2.0 because it follows its Tatoeba sources: English sentences are CC0 (public domain), Italian sentences are CC BY 2.0 FR. Each row has its own license column and every contributor is listed in ATTRIBUTION.md. Labels and code are Apache-2.0.

Limitations

• The errors are synthetic and cover only five families, not everything learners do.
• Tatoeba sentences are short and conversational.
• Some errors (like repeated words) are easy to spot with shallow features, so try a TF-IDF baseline before claiming a model learned syntax.

Citation

@misc{lapsus,
  title  = {Lapsus: a bilingual grammatical acceptability dataset for teaching NLP},
  howpublished = {\url{https://github.com/MorenoLaQuatra/lapsus}},
  year   = {2026}
}