Cross-lingual sentiment analysis (CLSA) leverages one or several source languages to help
the low-resource languages to perform sentiment analysis. Therefore, the problem of lack of …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …

Map** word embeddings of different languages into a single space has multiple
applications. In order to map from a source space into a target space, a common approach is …

Word embeddings are well known to capture linguistic regularities of the language on which
they are trained. Researchers also observe that these regularities can transfer across …

In cross-lingual transfer, NLP models over one or more source languages are applied to a
low-resource target language. While most prior work has used a single source model or a …

We propose learning sentiment-specific word embeddings dubbed sentiment embeddings
in this paper. Existing word embedding learning algorithms typically only use the contexts of …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …

Despite interest in using cross-lingual knowledge to learn word embeddings for various
tasks, a systematic comparison of the possible approaches is lacking in the literature. We …

Cross-lingual natural language processing hinges on the premise that there exists
invariance across languages. At the word level, researchers have identified such invariance …

The performance of multilingual pretrained models is highly dependent on the availability of
monolingual or parallel text present in a target language. Thus, the majority of the world's …