--- pretty_name: TEDMulti paperswithcode_id: null dataset_info: features: - name: translations dtype: translation_variable_languages: languages: - ar - az - be - bg - bn - bs - calv - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - fr-ca - gl - he - hi - hr - hu - hy - id - it - ja - ka - kk - ko - ku - lt - mk - mn - mr - ms - my - nb - nl - pl - pt - pt-br - ro - ru - sk - sl - sq - sr - sv - ta - th - tr - uk - ur - vi - zh - zh-cn - zh-tw num_languages: 60 - name: talk_name dtype: string config_name: plain_text splits: - name: test num_bytes: 23364983 num_examples: 7213 - name: train num_bytes: 748209995 num_examples: 258098 - name: validation num_bytes: 19435383 num_examples: 6049 download_size: 352222045 dataset_size: 791010361 --- # Dataset Card for "ted_multi" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/neulab/word-embeddings-for-nmt](https://github.com/neulab/word-embeddings-for-nmt) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 352.23 MB - **Size of the generated dataset:** 791.01 MB - **Total amount of disk used:** 1.14 GB ### Dataset Summary Massively multilingual (60 language) data set derived from TED Talk transcripts. Each record consists of parallel arrays of language and text. Missing and incomplete translations will be filtered out. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 352.23 MB - **Size of the generated dataset:** 791.01 MB - **Total amount of disk used:** 1.14 GB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls", "translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `translations`: a multilingual `string` variable, with possible languages including `ar`, `az`, `be`, `bg`, `bn`. - `talk_name`: a `string` feature. ### Data Splits | name |train |validation|test| |----------|-----:|---------:|---:| |plain_text|258098| 6049|7213| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{qi-EtAl:2018:N18-2, author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham}, title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?}, booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)}, month = {June}, year = {2018}, address = {New Orleans, Louisiana}, publisher = {Association for Computational Linguistics}, pages = {529--535}, abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.}, url = {http://www.aclweb.org/anthology/N18-2084} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.