Findings of the AmericasNLP 2021 shared task on open machine translation for indigenous languages of the Americas
This paper presents the results of the 2021 Shared Task on Open Machine Translation for
Indigenous Languages of the Americas. The shared task featured two independent tracks …
Indigenous Languages of the Americas. The shared task featured two independent tracks …
When is multilinguality a curse? language modeling for 250 high-and low-resource languages
Multilingual language models are widely used to extend NLP systems to low-resource
languages. However, concrete evidence for the effects of multilinguality on language …
languages. However, concrete evidence for the effects of multilinguality on language …
Transcending language boundaries: Harnessing llms for low-resource language translation
Large Language Models (LLMs) have demonstrated remarkable success across a wide
range of tasks and domains. However, their performance in low-resource language …
range of tasks and domains. However, their performance in low-resource language …
Goldfish: Monolingual language models for 350 languages
For many low-resource languages, the only available language models are large
multilingual models trained on many languages simultaneously. However, using FLORES …
multilingual models trained on many languages simultaneously. However, using FLORES …
Apnn-tc: Accelerating arbitrary precision neural networks on ampere gpu tensor cores
Over the years, accelerating neural networks with quantization has been widely studied.
Unfortunately, prior efforts with diverse precisions (eg, 1-bit weights and 2-bit activations) are …
Unfortunately, prior efforts with diverse precisions (eg, 1-bit weights and 2-bit activations) are …
Not always about you: Prioritizing community needs when developing endangered language technology
Z Liu, C Richardson, R Hatcher Jr… - arXiv preprint arXiv …, 2022 - arxiv.org
Languages are classified as low-resource when they lack the quantity of data necessary for
training statistical and machine learning tools and models. Causes of resource scarcity vary …
training statistical and machine learning tools and models. Causes of resource scarcity vary …
Hierarchical phrase-based sequence-to-sequence learning
We describe a neural transducer that maintains the flexibility of standard sequence-to-
sequence (seq2seq) models while incorporating hierarchical phrases as a source of …
sequence (seq2seq) models while incorporating hierarchical phrases as a source of …
Neural machine translation for the indigenous languages of the Americas: An introduction
Neural models have drastically advanced state of the art for machine translation (MT)
between high-resource languages. Traditionally, these models rely on large amounts of …
between high-resource languages. Traditionally, these models rely on large amounts of …
Compare encoder-decoder, encoder-only, and decoder-only architectures for text generation on low-resource datasets
Natural language generation (NLG) tasks have received significant research attention in
recent years. For tackling various NLG tasks, the Transformer [27] is now consensus to be …
recent years. For tackling various NLG tasks, the Transformer [27] is now consensus to be …
BLEU Skies for Endangered Language Revitalization: Lemko Rusyn and Ukrainian Neural AI Translation Accuracy Soars
P Orynycz - International Conference on Human-Computer …, 2023 - Springer
Accelerating global language loss, associated with elevated incidence of illicit substance
use, type 2 diabetes, binge drinking, and assault, as well as sixfold higher youth suicide …
use, type 2 diabetes, binge drinking, and assault, as well as sixfold higher youth suicide …