Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions

2016 ◽  
Author(s):  
Haihua Xu ◽  
Hang Su ◽  
Chongjia Ni ◽  
Xiong Xiao ◽  
Hao Huang ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Acoustic Models ◽  
Low Resource ◽  
Cross Lingual ◽  
Resource Conditions

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.

Improving DNN Bluetooth Narrowband Acoustic Models by Cross-Bandwidth and Cross-Lingual Initialization

2017 ◽  
Author(s):  
Xiaodan Zhuang ◽  
Arnab Ghoshal ◽  
Antti-Veikko Rosti ◽  
Matthias Paulik ◽  
Daben Liu
Keyword(s):  
Acoustic Models ◽  
Cross Lingual

Improving thai-lao neural machine translation with similarity lexicon

2021 ◽  
pp. 1-10
Author(s):  
Zhiqiang Yu ◽  
Yuxin Huang ◽  
Junjun Guo
Keyword(s):  
Machine Translation ◽  
Semantic Information ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Translation Quality ◽  
Decoder Architecture ◽  
Baseline System ◽  
Input Sentence ◽  
Resource Conditions ◽  
Language Pair

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works.

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.

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