Building machine learning prediction models for a specific NLP task requires sufficient training data, which can be difficult to obtain less-resourced languages. Cross-lingual embeddings map word from language resource-rich so that model trained on data the also used in language. To produce cross-lingual mappings of recent contextual embeddings, anchor points between embedding spaces have words...

Cross-lingual text classification is the task of assigning labels to observed documents in a label-scarce target language domain by using a prediction model trained with labeled documents from a label-rich source language domain. Cross-lingual text classification is popularly studied in natural language processing area to reduce the expensive manual annotation effort required in the target lang...

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case wit...

Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hy...

Finding term translations as cross-lingual spelling variants on the fly is an important problem for cross-lingual information retrieval (CLIR). CLIR is typically approached by automatically translating a query into the target language. For an overview of cross-lingual information retrieval, see [1]. When automatically translating the query, specialized terminology is often missing from the tran...

Cross-lingual relevance modelling (CLRLM) is a state-of-the-art technique for cross-lingual information retrieval (CLIR) which integrates query term disambiguation and expansion in a unified framework, to directly estimate a model of relevant documents in the target language starting with a query in the source language. However, CLRLM involves integrating a translation model either on the docum...

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compu...

Various lexical resources are being published in RDF. To enhance the usability of these resources, identical resources in different data sets should be linked. If lexical resources are described in different natural languages, then techniques to deal with multilinguality are required for interlinking. In this paper, we evaluate machine translation for interlinking concepts, i.e., generic entiti...

This research analyzes linguistic barriers and cross-lingual interaction through link analysis of more than 100,000 blogs discussing the 2010 Haitian earthquake in English, Spanish, and Japanese. In addition, cross-lingual hyperlinks are qualitatively coded. This study finds English-language blogs are significantly less likely to link cross-lingually than Spanish or Japanese blogs. However, blo...

Automatically generating a natural language description of an image is a fundamental problem in artificial intelligence. This task involves both computer vision and natural language processing and is called “image caption generation.” Research on image caption generation has typically focused on taking in an image and generating a caption in English as existing image caption corpora are mostly ...