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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search 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 BASE Hide details 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 BASE Show details 3 Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models ... Wada, Takashi; Iwata, Tomoharu. - : arXiv, 2018 BASE Show details

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