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Found 10 papers, 4 papers with code
Learning Cross-lingual Embeddings from Twitter via Distant Supervision
Cross-lingual embeddings represent the meaning of words from different languages in the same vector space.
Cross-Lingual Word Embeddings for Turkic Languages
Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together.
Towards robust word embeddings for noisy texts
Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media.
Improving Cross-Lingual Word Embeddings by Meeting in the Middle
Cross-lingual word embeddings are becoming increasingly important in multilingual NLP.
Comparing Neural- and N-Gram-Based Language Models for Word Segmentation
The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not.
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning
Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language.
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together.
Cross-Lingual Natural Language Inference
Cross-Lingual Word Embeddings
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On the performance of phonetic algorithms in microtext normalization
As an answer, microtext normalization consists in transforming those non-standard microtexts into standard well-written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing.
