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Datasets:
PoetryMTEB
/
PoetryTranslationEMNLP2021

Tasks:
Translation
Sentence Similarity
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-scoring
document-retrieval
Languages:
English
German
Dutch
Size:
100K - 1M
Tags:
poetry
bitext-mining
retrieval
parallel-corpus
multilingual
mteb
Libraries:
Datasets
pandas
Polars
License:
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metadata 
license: cc-by-nc-4.0
task_categories:
  - translation
  - sentence-similarity
task_ids:
  - semantic-similarity-scoring
  - document-retrieval
language:
  - en
  - de
  - nl
  - it
  - pt
  - ru
  - es
  - ro
  - sv
  - uk
multilinguality:
  - multilingual
  - translation
size_categories:
  - 100K<n<1M
pretty_name: PoetryTranslationEMNLP2021
tags:
  - poetry
  - bitext-mining
  - retrieval
  - parallel-corpus
  - multilingual
  - mteb
  - poetrymteb
  - sentence-similarity
configs:
  - config_name: deu-eng
    data_files:
      - split: train
        path: deu-eng/train-*
      - split: validation
        path: deu-eng/validation-*
      - split: test
        path: deu-eng/test-*
    default: true
  - config_name: ita-eng
    data_files:
      - split: train
        path: ita-eng/train-*
      - split: validation
        path: ita-eng/validation-*
      - split: test
        path: ita-eng/test-*
  - config_name: nld-eng
    data_files:
      - split: train
        path: nld-eng/train-*
      - split: validation
        path: nld-eng/validation-*
      - split: test
        path: nld-eng/test-*
  - config_name: por-eng
    data_files:
      - split: train
        path: por-eng/train-*
      - split: validation
        path: por-eng/validation-*
      - split: test
        path: por-eng/test-*
  - config_name: ron-eng
    data_files:
      - split: test
        path: ron-eng/test-*
  - config_name: rus-eng
    data_files:
      - split: train
        path: rus-eng/train-*
      - split: validation
        path: rus-eng/validation-*
      - split: test
        path: rus-eng/test-*
  - config_name: spa-eng
    data_files:
      - split: train
        path: spa-eng/train-*
      - split: validation
        path: spa-eng/validation-*
      - split: test
        path: spa-eng/test-*
  - config_name: swe-eng
    data_files:
      - split: test
        path: swe-eng/test-*
  - config_name: ukr-eng
    data_files:
      - split: test
        path: ukr-eng/test-*
dataset_info:
  - config_name: deu-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
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        num_examples: 16999
      - name: validation
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        num_examples: 1051
      - name: test
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        num_examples: 1296
    download_size: 1680748
    dataset_size: 2114992
  - config_name: ita-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: train
        num_bytes: 3833784
        num_examples: 34525
      - name: validation
        num_bytes: 219099
        num_examples: 1998
      - name: test
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        num_examples: 315
    download_size: 3188013
    dataset_size: 4084056
  - config_name: nld-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: train
        num_bytes: 2506045
        num_examples: 23376
      - name: validation
        num_bytes: 106021
        num_examples: 1000
      - name: test
        num_bytes: 16115
        num_examples: 159
    download_size: 2091864
    dataset_size: 2628181
  - config_name: por-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: train
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        num_examples: 15200
      - name: validation
        num_bytes: 77272
        num_examples: 700
      - name: test
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        num_examples: 140
    download_size: 1272443
    dataset_size: 1648060
  - config_name: ron-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: test
        num_bytes: 11418
        num_examples: 100
    download_size: 10703
    dataset_size: 11418
  - config_name: rus-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: train
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        num_examples: 50001
      - name: validation
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        num_examples: 4186
      - name: test
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        num_examples: 548
    download_size: 4584359
    dataset_size: 6456537
  - config_name: spa-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: train
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        num_examples: 37744
      - name: validation
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        num_examples: 2059
      - name: test
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        num_examples: 536
    download_size: 3280916
    dataset_size: 4079475
  - config_name: swe-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: test
        num_bytes: 9407
        num_examples: 96
    download_size: 9435
    dataset_size: 9407
  - config_name: ukr-eng
    features:
      - name: id
        dtype: string
      - name: source_text
        dtype: string
      - name: target_text
        dtype: string
    splits:
      - name: test
        num_bytes: 11031
        num_examples: 99
    download_size: 10116
    dataset_size: 11031

PoetryTranslationEMNLP2021

Poetry-to-English parallel poetry bitext, reformatted for PoetryMTEB Bitext Mining / cross-lingual retrieval evaluation.

This Hub release is a structured redistribution of the parallel poetry corpus released with the EMNLP 2021 paper Don't Go Far Off: An Empirical Study on Neural Poetry Translation (Chakrabarty et al., 2021; source repository: tuhinjubcse/PoetryTranslationEMNLP2021).

Dataset Card

Item Description
Source PoetryTranslationEMNLP2021 (corpus_v2.1 + testdatawithtranslations); paper Chakrabarty et al., EMNLP 2021
Languages Source → English: German (deu), Dutch (nld), Italian (ita), Portuguese (por), Russian (rus), Spanish (spa); test-only: Romanian (ron), Swedish (swe), Ukrainian (ukr)
Size ~192K aligned pairs across 9 configs (see per-config table below)
Label / field type Parallel bitext (no classification labels). Fields: id (string), source_text (string), target_text (string, English)
Splits train / validation (6 language pairs from corpus_v2.1); test (all 9 pairs from testdatawithtranslations)
Construction See Construction method
License CC BY-NC 4.0
Evaluation metrics Bitext mining / retrieval: Recall@k, nDCG@k (PoetryMTEB). Original MT study also reports BLEU, BERTScore, COMET, and human faithfulness / poetic-style ratings

Features

Field Type Description
id string Unique pair identifier
source_text string Non-English source poetry text
target_text string English target / gold translation

Aligned with PoetryMTEB MultilingualPoetryBitextMining schema for drop-in bitext mining evaluation.

Configs, languages & size

Each Hugging Face config is one language pair {src}-eng (ISO 639-3).

Config Source lang Train Validation Test Total
deu-eng German 16,999 1,051 1,296 19,346
ita-eng Italian 34,525 1,998 315 36,838
nld-eng Dutch 23,376 1,000 159 24,535
por-eng Portuguese 15,200 700 140 16,040
rus-eng Russian 50,001 4,186 548 54,735
spa-eng Spanish 37,744 2,059 536 40,339
ron-eng Romanian — — 100 100
swe-eng Swedish — — 96 96
ukr-eng Ukrainian — — 99 99
All 177,845 10,994 3,289 192,128

Construction method

  1. Upstream corpus
    Parallel poetry translations collected and released by Chakrabarty et al. (EMNLP 2021):

    • corpus_v2.1: sentence-level {lang}train.json / {lang}valid.json for 6 languages → English
    • testdatawithtranslations: poem-level source (test*) and gold English (gold*) for 6 + 3 additional languages

  2. This release (PoetryMTEB packaging)

    • train / validation: read sentence pairs from corpus_v2.1, map fields to source_text / target_text, assign stable ids, write Parquet shards per config.
    • test: for each poem, split source and gold into non-empty lines; align lines within the poem (truncate to the shorter side if lengths differ); emit one bitext row per aligned line.
    • Schema unified with PoetryMTEB Bitext Mining (id, source_text, target_text).

We do not redistribute model-generated candidates from the original repo; only human gold references are used as target_text on the test split.

Evaluation metrics

Primary (PoetryMTEB Bitext Mining / retrieval):

Metric Role
Recall@k Fraction of queries whose gold translation is retrieved in top-k
nDCG@k Ranking quality against gold bitext partners

Reported in the original EMNLP 2021 study (MT setting): BLEU, BERTScore, COMET, plus human evaluation of meaning faithfulness and poetic style (paper).

How to load 

from datasets import load_dataset

ds = load_dataset("PoetryMTEB/PoetryTranslationEMNLP2021", "deu-eng")
print(ds)
print(ds["test"][0])
# {'id': '...', 'source_text': '...', 'target_text': '...'}

Available configs: deu-eng, ita-eng, nld-eng, por-eng, rus-eng, spa-eng, ron-eng, swe-eng, ukr-eng.

License

Distributed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Please also respect any terms associated with the original GitHub release.

Citation

If you use this dataset, please cite the original paper and, when relevant, this PoetryMTEB redistribution.

Paper (required):

@inproceedings{chakrabarty-etal-2021-dont,
    title = "Don{'}t Go Far Off: An Empirical Study on Neural Poetry Translation",
    author = "Chakrabarty, Tuhin  and
      Saakyan, Arkadiy  and
      Muresan, Smaranda",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.577/",
    doi = "10.18653/v1/2021.emnlp-main.577",
    pages = "7253--7265"
}

Dataset source repository: