MultiIdiom is a seven-language idiom dataset (English, Spanish, Hindi, Telugu, Indonesian, French, German) for joint idiom detection and span localization — predicting whether an idiom in a sentence is used idiomatically or literally, and marking its exact character-level span. 14,505 idioms / 34,184 senses / 112,317 examples, constructed via a six-stage LLM-assisted pipeline from Wiktionary-seeded idiom entries. English/Spanish/Hindi/Telugu are split 80/10/10 at the idiom-type level; Indonesian/French/German are held out entirely as zero-shot test languages. English and Telugu were evaluated and found to have an idiomaticity label agreement of 203/221 = 91.86% for English and 90/102 = 88.24% for Telugu.
Data Splits
| Split |
EN |
ES |
HI |
TE |
ID |
FR |
DE |
Total |
| Train |
2,030 |
2,034 |
508 |
506 |
— |
— |
— |
5,078 |
| Dev |
252 |
254 |
62 |
61 |
— |
— |
— |
629 |
| Test |
251 |
254 |
62 |
62 |
325 |
800 |
800 |
2,554 |
| Total |
2,533 |
2,542 |
632 |
629 |
325 |
800 |
800 |
8,261 |
Indonesian, French, and German are zero-shot evaluation languages (test-only, no train/dev). French/German test counts (800 each) match the exact evaluation set used in the companion IdiomBERT paper. corpus_<lang>.jsonl files contain the full per-language pool (all senses/examples, no split) — use these instead of the official test split if you need more data than the paper evaluated on.
Full Corpus (corpus_<lang>.jsonl — all senses/examples, no split)
| Lang |
Idioms |
Senses |
Examples |
Idiomatic |
Literal |
| English |
9,638 |
23,140 |
76,082 |
57,373 |
18,709 |
| Spanish |
3,185 |
7,518 |
24,767 |
18,480 |
6,287 |
| Hindi |
183 |
409 |
1,420 |
660 |
760 |
| Telugu |
103 |
210 |
734 |
318 |
416 |
| Indonesian |
46 |
95 |
328 |
156 |
172 |
| French |
332 |
703 |
2,038 |
1,218 |
820 |
| German |
1,018 |
2,109 |
6,948 |
3,656 |
3,292 |
| Total |
14,505 |
34,184 |
112,317 |
81,861 |
30,456 |
