Co-occurrence Weight Selection in Generation of Word Embeddings for Low Resource Languages

2019 ◽  
Vol 18 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Veysel Yücesoy ◽  
Aykut Koç
Keyword(s):  
Word Embeddings ◽  
Low Resource

scholarly journals Anchor-based Bilingual Word Embeddings for Low-Resource Languages

2021 ◽  
Author(s):  
Tobias Eder ◽  
Viktor Hangya ◽  
Alexander Fraser
Keyword(s):  
Word Embeddings ◽  
Low Resource

scholarly journals Incorporating word embeddings in unsupervised morphological segmentation

2020 ◽  
pp. 1-21
Author(s):  
Ahmet Üstün ◽  
Burcu Can
Keyword(s):  
Semantic Information ◽  
Maximum A Posteriori ◽  
Word Embeddings ◽  
A Posteriori ◽  
Low Resource ◽  
A Posteriori Estimate ◽  
Morphological Segmentation ◽  
Vector Representations ◽  
Turkish Language ◽  
Maximum A Posteriori Estimate

Abstract We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.

scholarly journals Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

2021 ◽  
Author(s):  
Takashi Wada ◽  
Tomoharu Iwata ◽  
Yuji Matsumoto ◽  
Timothy Baldwin ◽  
Jey Han Lau
Keyword(s):  
Word Embeddings ◽  
Parallel Corpora ◽  
Low Resource ◽  
Cross Lingual

scholarly journals Cross-Lingual Word Embeddings for Low-Resource Language Modeling

2017 ◽  
Author(s):  
Oliver Adams ◽  
Adam Makarucha ◽  
Graham Neubig ◽  
Steven Bird ◽  
Trevor Cohn
Keyword(s):  
Language Modeling ◽  
Word Embeddings ◽  
Low Resource ◽  
Cross Lingual

scholarly journals Tackling neural machine translation in low-resource settings: a Portuguese case study

2021 ◽  
Author(s):  
Arthur T. Estrella ◽  
João B. O. Souza Filho
Keyword(s):  
Machine Translation ◽  
Data Augmentation ◽  
Word Embeddings ◽  
Effective Solution ◽  
Computational Power ◽  
Limited Data ◽  
Neural Machine Translation ◽  
Low Resource

Neural machine translation (NMT) nowadays requires an increasing amount of data and computational power, so succeeding in this task with limited data and using a single GPU might be challenging. Strategies such as the use of pre-trained word embeddings, subword embeddings, and data augmentation solutions can potentially address some issues faced in low-resource experimental settings, but their impact on the quality of translations is unclear. This work evaluates some of these strategies on two low-resource experiments beyond just reporting BLEU: errors are categorized on the Portuguese-English pair with the help of a translator, considering semantic and syntactic aspects. The BPE subword approach has shown to be the most effective solution, allowing a BLEU increase of 59% p.p. compared to the standard Transformer.

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