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Hits 1 – 3 of 3
1
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
., Yuji
;
Baldwin, Timothy
;
Iwata, Tomoharu
;
Lau, Jey Han
;
Wada, Takashi
. - : Underline Science Inc., 2021
Abstract:
We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations even in extremely low-resource ...
Keyword:
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
;
Speech Processing
URL:
https://dx.doi.org/10.48448/d3e5-gv03
https://underline.io/lecture/39650-learning-contextualised-cross-lingual-word-embeddings-and-alignments-for-extremely-low-resource-languages-using-parallel-corpora
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2
Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora ...
Wada, Takashi
;
Iwata, Tomoharu
;
Matsumoto, Yuji
. - : arXiv, 2020
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3
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models ...
Wada, Takashi
;
Iwata, Tomoharu
. - : arXiv, 2018
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