Info hash | 3ed5f07cfb390e638d9de12a2adcfb1e51c8fbb9 |
Last mirror activity | 2:35 ago |
Size | 10.21MB (10,214,252 bytes) |
Added | 2023-05-06 01:47:20 |
Views | 30 |
Hits | 409 |
ID | 5007 |
Type | single |
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Uploaded by | ResearchRat |
Filename | BOLT Egyptian Arabic SMS/Chat Parallel Training Data |
Mirrors | 5 complete, 0 downloading = 5 mirror(s) total [Log in to see full list] |
BOLT Egyptian Arabic SMS/Chat Parallel Training Data | 10.21MB |
Type: Dataset
Tags: machine translation, nlp, english, Arabic, egyptian arabic, BOLT, Linguistic Corpora, SMS, parallel training data
Bibtex:
Tags: machine translation, nlp, english, Arabic, egyptian arabic, BOLT, Linguistic Corpora, SMS, parallel training data
Bibtex:
@article{, title= {BOLT Egyptian Arabic SMS/Chat Parallel Training Data - LDC2021T15}, journal= {}, author= {Jennifer Tracey and Dana Delgado and Song Chen and Stephanie Strassel}, year= {2021}, doi= {10.35111/k4bf-hh16}, url= {https://doi.org/10.35111/k4bf-hh16}, abstract= {# BOLT Egyptian Arabic SMS/Chat Parallel Training Data | **Item Name:** | BOLT Egyptian Arabic SMS/Chat Parallel Training Data | |---|---| | **Author(s):** | Jennifer Tracey, Dana Delgado, Song Chen, Stephanie Strassel | | **LDC Catalog No.:** | LDC2021T15 | | **ISBN:** | 1-58563-971-0 | | **ISLRN:** | 063-821-462-782-5 | | **DOI:** | https://doi.org/10.35111/k4bf-hh16 | | **Release Date:** | August 16, 2021 | | **Member Year(s):** | 2021 | | **DCMI Type(s):** | Text | | **Data Source(s):** | text chat conversations | | **Project(s):** | BOLT | | **Application(s):** | machine translation | | **Language(s):** | Egyptian Arabic, English | | **Language ID(s):** | arz, eng | | **Citation:** | Tracey, Jennifer, et al. BOLT Egyptian Arabic SMS/Chat Parallel Training Data LDC2021T15. Web Download. Philadelphia: Linguistic Data Consortium, 2021. | ## Introduction BOLT Egyptian Arabic SMS/Chat Parallel Training Data was developed by the Linguistic Data Consortium (LDC) and consists of approximately 723,000 tokens of Egyptian Arabic SMS/Chat data collected for the DARPA BOLT program along with their corresponding English translations. The DARPA [BOLT](https://www.ldc.upenn.edu/collaborations/current-projects/bolt) (Broad Operational Language Translation) program developed machine translation and information retrieval for less formal genres, focusing particularly on user-generated content. LDC supported the BOLT program by collecting informal data sources -- discussion forums, text messaging and chat -- in Chinese, Egyptian Arabic and English. The collected data was translated and annotated for various tasks including word alignment, treebanking, propbanking and co-reference. ## Data The source data in this release was collected using two methods: new collection via LDC's collection platform, and donation of SMS or chat archives from BOLT collection participants. All data were reviewed manually to exclude any messages/conversations that were not in the target language or that had sensitive content, such as personal identifying information. Data was manually selected for translation. Messages/conversations were arranged in chronological order, segmented into sentence units (all or portions of message threads depending on their length), and assigned to translation vendors. Translators followed LDC's BOLT translation guidelines. Source and translation files are presented in UTF-8 encoded XML format. ## Sponsorship This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-11-C-0145. The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. ## Updates None at this time. ## Related - Archive.org Mirror: https://archive.org/details/LDC2021T15 - [BOLT Egyptian Arabic Treebank Conversational Telephone Speech LDC2021T12](https://academictorrents.com/details/65e7183bec20d506c07be8f708a403da21b2ddcb) }, keywords= {Machine Translation, Linguistic Corpora, SMS, parallel training data, Egyptian Arabic, Arabic, English, NLP, BOLT}, terms= {}, license= {}, superseded= {} }