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Overview of the NIST 2016 LoReHLT Evaluation

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Author(s)

Audrey N. Tong, Lukasz L. Diduch, Jonathan G. Fiscus, Yasaman Haghpanah, Shudong Huang, David M. Joy, Kay Peterson, Ian M. Soboroff

Abstract

Initiated in conjunction with DARPA's Low Resource Languages for Emergent Incidents (LORELEI) Program, the NIST LoReHLT (Low Re-source Human Language Technology) evaluation series seeks to incubate research on fundamental natural language processing tasks in under-resourced languages. While part of the LORELEI program, LoReHLT is an open evaluation workshop that anyone may participate and had its fi rst evaluation in July 2016. A total of eight teams out of 21 registrants participated in the evaluation over three tasks - machine translation, named entity recognition, and situation frame - in the surprise language Uyghur.
Citation
Machine Translation Special Issue NLP in Low Resource Languages
Pub Type
Journals

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Keywords

low resource human language technology evaluation, machine translation evaluation, named entity recognition evaluation, situation frame evaluation, Uyghur
Information technology, Data and informatics and Human language technology

Citation

Tong, A. , Diduch, L. , Fiscus, J. , Haghpanah, Y. , Huang, S. , Joy, D. , Peterson, K. and Soboroff, I. (2017), Overview of the NIST 2016 LoReHLT Evaluation, Machine Translation Special Issue NLP in Low Resource Languages, [online], https://doi.org/10.1007/s10590-017-9200-8 (Accessed April 19, 2024)

Created November 13, 2017, Updated November 10, 2018