language:udm region:us
---
configs:
- config_name: default
  data_files:
  - split: sentences
    path: data/sentences-*
dataset_info:
  features:
  - name: rus
    dtype: string
  - name: udm
    dtype: string
  splits:
  - name: sentences
    num_bytes: 129728
    num_examples: 250
  download_size: 72479
  dataset_size: 129728
language:
- udm
---
# FLORES-250, Russian and Udmurt sentences

Compared to the original FLORES-250, in the Russian version the sentence 

`Вечер начал певец Санджу Шарма, за ним выступил Джай Шанкар Чаудхари. esented the chhappan bhog bhajan также. Ему аккомпанировал певец Раджу Кханделвал`

changed to 

`Вечер начал певец Санджу Шарма, за ним выступил Джай Шанкар Чаудхари. Ему аккомпанировал певец Раджу Кханделвал`.

## Usage

```py
from datasets import load_dataset

dataset = load_dataset("udmurtNLP/flores-250-rus-udm")
```

## Citation

```
@inproceedings{yankovskaya-etal-2023-machine,
    title = "Machine Translation for Low-resource {F}inno-{U}gric Languages",
    author = {Yankovskaya, Lisa  and
      Tars, Maali  and
      T{\"a}ttar, Andre  and
      Fishel, Mark},
    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, Faroe Islands",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2023.nodalida-1.77",
    pages = "762--771",
    abstract = "This paper focuses on neural machine translation (NMT) for low-resource Finno-Ugric languages. Our contributions are three-fold: (1) we extend existing and collect new parallel and monolingual corpora for 20 languages, (2) we expand the 200-language translation benchmark FLORES-200 with manual translations into nine new languages, and (3) we present experiments using the collected data to create NMT systems for the included languages and investigate the impact of back-translation data on the NMT performance for low-resource languages. Experimental results show that carefully selected limited amounts of back-translation directions yield the best results in terms of translation scores, for both high-resource and low-resource output languages.",
}
```