Techniques for multi-lingual and cross-lingual speech recognition can help in low resource scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to-end approaches, in particular sequence-based techniques, are attractive because of their simplicity and elegance. While it is possible to integrate traditional multi-lingual bottleneck feature extractors as front-ends...

The cross-sectional canal morphology and minimum widths of buccal and lingual canal walls were studied in 20 mandibular molars with C-shaped roots and canal orifices. The roots were mounted in clear resin blocks and sectioned transversely at 1-mm intervals. A total of 154 cross-sections were evaluated with an image analyzer. Twelve different longitudinal canal configurations were identified. Th...

The present study was aimed to determine the topographic relationship between root apex of the mesially and horizontally impacted mandibular third molar and lingual plate of mandible. The original cone beam computed tomography (CBCT) data of 364 teeth from 223 patients were retrospectively collected and analyzed. The topographic relationship between root apex and lingual plate on cross-sectiona...

How to construct relationship between bilingual texts is important to effectively processing multi-lingual text data and cross language barriers. Cross-lingual latent semantic indexing (CL-LSI) corpus-based doesnot fully take into account bilingual semantic relationship. The paper proposes a new model building semantic relationship of bilingual parallel document via partial least squares (PLS)....

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across div...

Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we sugg...

Wikipedia is an online multilingual encyclopedia that contains a very large number of articles covering most written languages. However, one critical issue for Wikipedia is that the pages in different languages are rarely linked except for the cross-lingual link between pages about the same subject. This could pose serious difficulties to humans and machines who try to seek information from dif...

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scarce target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To addres...

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translat...

This article deals with the problem of Cross-Lingual Text Categorization (CLTC), which arises when documents in different languages must be classified according to the same classification tree. We describe practical and cost-effective solutions for automatic Cross-Lingual Text Categorization, both in case a sufficient number of training examples is available for each new language and in the cas...