Datasets

38

alt

The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under ASEAN IVO as desc...

bianet

A parallel news corpus in Turkish, Kurdish and English. Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. 3 languages, 3 bitexts total number of files: 6 total number of tokens: 2.25M total number of sentence fragments: 0.14M

bsd_ja_en

This is the Business Scene Dialogue (BSD) dataset, a Japanese-English parallel corpus containing written conversations in various business scenarios. The dataset was constructed in 3 steps: 1) selecting business scenes, 2) writing monolingual conversation scenarios according to the selected scenes, and 3) translating the scenarios into th...

chr_en

ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.

europa_eac_tm

In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM. EAC-TM covers up to 26 languages: 22 off...

europa_ecdc_tm

In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. This resource bears the name EAC Translation Memory, short EAC-TM. ECDC-TM covers 25 languages: th...

generated_reviews_enth

`generated_reviews_enth` Generated product reviews dataset for machine translation quality prediction, part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated...

hind_encorp

HindEnCorp parallel texts (sentence-aligned) come from the following sources: Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy,...

hrenwac_para

The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bi...

id_panl_bppt

Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international...

menyo20k_mt

MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences...

mkqa

We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human...

ms_terms

The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in ...

annotations_creators: expert-generated language_creators: expert-generated languages: af languages: sq languages: am languages: ar languages: hy languages: as languages: az languages: bn languages: other-bn-india languages: eu languages: be languages: bs languages: other-bs-latin languages: bg languages: ca languages: ku languages: chr languages: zh languages: other-zh-Hant_HK languages: other-zh-Hant_TW languages: hr languages: cs languages: da languages: prs languages: nl languages: en languages: et languages: fil languages: fi languages: fr languages: other-fr_CA languages: gl languages: ka languages: de languages: el languages: gu languages: ha languages: he languages: hi languages: hu languages: is languages: ig languages: id languages: iu languages: ga languages: xh languages: zu languages: it languages: ja languages: quc languages: kn languages: kk languages: km languages: rw languages: swh languages: knn languages: ko languages: ky languages: lo languages: lv languages: lt languages: lb languages: mk languages: other-ms-brunei (Brunei Darus.) languages: ms languages: ml languages: mt languages: mi languages: mr languages: mn languages: ne languages: nb languages: nn languages: ory languages: pst languages: fa languages: pl languages: other-pt-br languages: pt languages: pa languages: pa languages: qu languages: ro languages: ru languages: gd languages: other-sr-bih languages: sr languages: other-sr-latin languages: st languages: tn languages: sd languages: si languages: sk languages: sl languages: es languages: other-es-MX languages: sv languages: tg languages: ta languages: tt languages: te languages: th languages: ti languages: tr languages: tk languages: uk languages: ur languages: ug languages: uz languages: other-valencian languages: vi languages: guc languages: cy languages: wo languages: yo licenses: ms-pl multilinguality: multilingual multilinguality: translation size_categories: 10K<n<100K source_datasets: original task_categories: conditional-text-generation task_ids: machine-translation

msr_zhen_translation_parity

Translator Human Parity Data Human evaluation results and translation output for the Translator Human Parity Data release, as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/. The Translator Human Parity Data release contains all human evaluation results and translations related to our paper "Achieving...

opus100

OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.

task_categories: sequence-modeling multilinguality: translation task_ids: language-modeling languages: af languages: en languages: am languages: en languages: an languages: en languages: ar languages: en languages: as languages: en languages: az languages: en languages: be languages: en languages: bg languages: en languages: bn languages: en languages: br languages: en languages: bs languages: en languages: ca languages: en languages: cs languages: en languages: cy languages: en languages: da languages: en languages: de languages: en languages: dz languages: en languages: el languages: en languages: en languages: eo languages: en languages: es languages: en languages: et languages: en languages: eu languages: en languages: fa languages: en languages: fi languages: en languages: fr languages: en languages: fy languages: en languages: ga languages: en languages: gd languages: en languages: gl languages: en languages: gu languages: en languages: ha languages: en languages: he languages: en languages: hi languages: en languages: hr languages: en languages: hu languages: en languages: hy languages: en languages: id languages: en languages: ig languages: en languages: is languages: en languages: it languages: en languages: ja languages: en languages: ka languages: en languages: kk languages: en languages: km languages: en languages: ko languages: en languages: kn languages: en languages: ku languages: en languages: ky languages: en languages: li languages: en languages: lt languages: en languages: lv languages: en languages: mg languages: en languages: mk languages: en languages: ml languages: en languages: mn languages: en languages: mr languages: en languages: ms languages: en languages: mt languages: en languages: my languages: en languages: nb languages: en languages: ne languages: en languages: nl languages: en languages: nn languages: en languages: no languages: en languages: oc languages: en languages: or languages: en languages: pa languages: en languages: pl languages: en languages: ps languages: en languages: pt languages: en languages: ro languages: en languages: ru languages: en languages: rw languages: en languages: se languages: en languages: sh languages: en languages: si languages: en languages: sk languages: en languages: sl languages: en languages: sq languages: en languages: sr languages: en languages: sv languages: en languages: ta languages: en languages: te languages: en languages: tg languages: en languages: th languages: en languages: tk languages: en languages: tr languages: en languages: tt languages: en languages: ug languages: en languages: uk languages: en languages: ur languages: en languages: uz languages: en languages: vi languages: en languages: wa languages: en languages: xh languages: en languages: yi languages: en languages: yo languages: en languages: zh languages: en languages: zu annotations_creators: no-annotation source_datasets: extended size_categories: 10K<n<1M licenses: unknown

opus_finlex

The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.

para_pat

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 lan...

pib

This new dataset is the large scale sentence aligned corpus in 11 Indian languages, viz. CVIT-PIB corpus that is the largest multilingual corpus available for Indian languages.

poleval2019_mt

PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according topre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4). The task ...

refresd

The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level rationales. For any questions, write to ebriakou@cs.umd.edu.

scb_mt_enth_2020

scb-mt-en-th-2020: A Large English-Thai Parallel Corpus The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialo...

snow_simplified_japanese_corpus

About SNOW T15: The simplified corpus for the Japanese language. The corpus has 50,000 manually simplified and aligned sentences. This corpus contains the original sentences, simplified sentences and English translation of the original sentences. It can be used for automatic text simplification as well as translating simple Japanese into English...