scholarly journals Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR

2012 ◽  
Author(s):  
Pawel Swietojanski ◽  
Arnab Ghoshal ◽  
Steve Renals
Keyword(s):  
Knowledge Transfer ◽  
Cross Lingual

scholarly journals Language engineering for syntactic knowledge transfer

2012 ◽  
Vol 9 (3) ◽  
pp. 1231-1247 ◽  
Author(s):  
Mihaela Colhon
Keyword(s):  
Knowledge Transfer ◽  
Syntactic Parsing ◽  
Language Engineering ◽  
Syntactic Knowledge ◽  
Cross Lingual ◽  
Parallel Texts

In this paper we present a method for an English-Romanian treebank construction, together with the obtained evaluation results. The treebank is built upon a parallel English-Romanian corpus word-aligned and annotated at the morphological and syntactic level. The syntactic trees of the Romanian texts are generated by considering the syntactic phrases of the English parallel texts automatically resulted from syntactic parsing. The method reuses and adjusts existing tools and algorithms for cross-lingual transfer of syntactic constituents and syntactic trees alignment.

scholarly journals A Multi-media Approach to Cross-lingual Entity Knowledge Transfer

2016 ◽  
Author(s):  
Di Lu ◽  
Xiaoman Pan ◽  
Nima Pourdamghani ◽  
Shih-Fu Chang ◽  
Heng Ji ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Multi Media ◽  
Cross Lingual

scholarly journals Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 9547-9554
Author(s):  
Mozhi Zhang ◽  
Yoshinari Fujinuma ◽  
Jordan Boyd-Graber
Keyword(s):  
Knowledge Transfer ◽  
Text Classification ◽  
Document Classification ◽  
Training Data ◽  
Target Language ◽  
Source Language ◽  
Low Resource ◽  
Classification Framework ◽  
Related Language ◽  
Cross Lingual

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (caco) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. Experiments confirm that character-level knowledge transfer is more data-efficient than word-level transfer between related languages.

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