LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages

By Stephanie Strassel, Jennifer Tracey. 2016

131 citations recorded by the source corpus.

In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA’s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tagsets, and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.

Subfields: Artificial Intelligence, Computer Networks and Communications, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Beyond English-Centric Multilingual Machine Translation (2020, background): “, 2019), LORELEI (Strassel and Tracey, 2016), and ALT (Riza et al.”
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English (2019, background): “In terms of low-resource datasets, DARPA programs like LORELEI (Strassel and Tracey, 2016) have collected translations on several lowresource languages like English–Tagalog.”
Cross-Lingual Ability of Multilingual BERT: An Empirical Study (2019, methodology): “We use the Cross-lingual Natural Language Inference (XNLI) (Conneau et al., 2018) dataset to evaluate cross-lingual TE performance and LORELEI dataset (Strassel & Tracey, 2016) for Cross-Lingual NER.”
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (2021, background): “0 (Guzmán et al., 2019), LORELEI (Strassel and Tracey, 2016), ALT (Thu et al., 2016; Riza et al., 2016; Ding et al., 2016) and TICO-19 (Anastasopoulos et al., 2020), as well as datasets for speciﬁc languages such as Igbo (Ezeani et al., 2020) and Fon (Dossou and Emezue, 2020).”
Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English (2019, background): “In terms of low-resource data-sets, DARPA programs like LORELEI (Strassel and Tracey, 2016) have collected translations on several low resource languages like English–Tagalog.”
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023, background): “, 2018) (for Natural Language Inference in 14 languages), DARPA LORELEI (Strassel and Tracey, 2016) (for NER and Entity Linking in 34 languages), TyDi (Zhang et al.”
Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction (2017, methodology): “For Turkish, we utilize the preprocessing tools (tokenization and POS tagging) provided in LORELEI Language Packs (Strassel and Tracey, 2016), and its English side is preprocessed by NLTK.”
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages (2022, background): “Prior researchers have emphasized the need to explore the unique challenges of low-resource languages (Simpson et al., 2008; Strassel and Tracey, 2016; Cruz and Cheng, 2020; Lakew et al., 2020; Marivate et al., 2020; Magueresse et al., 2020; Goyal et al., 2021), while the growing number…”