Datasets:

nirmalendu01
/

ted_multi

language:
  - multilingual
license: cc-by-nc-nd-4.0
pretty_name: TED multi (4-way TSV mirror)
tags:
  - parallel-corpora
  - tedtalks
  - multilingual
  - re-host
size_categories:
  - 100K<n<1M

TED multi — TSV mirror

Faithful re-host of the original neulab/ted_multi TED Talks corpus, in the same row-aligned multi-way parallel TSV format that was distributed at https://www.phontron.com/data/ted_talks.tar.gz.

The HF Datasets script neulab/ted_multi is currently broken (_DATA_URL returns an SPA), which is why this mirror exists. It does not modify the data.

Files

all_talks_train.tsv — train split (≈258k rows).
all_talks_dev.tsv — dev split (≈6k rows).
all_talks_test.tsv — test split (≈7k rows).

Schema

Each TSV row has 60 language columns + talk_name + id (depending on header line — see the first line of each file). Missing translations for a row are written literally as __NULL__ (some legacy snapshots also use _ _ NULL _ _). When you need an N-way parallel subset, drop any row where any of the target language columns equals __NULL__.

Source / attribution

Qi, Y., Sachan, D., Felix, M., Padmanabhan, S., & Neubig, G. (2018). When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation? In NAACL.

Original distribution lived at https://www.phontron.com/data/ted_talks.tar.gz.