Probabilistic topic models are unsupervised generative models which model document
content as a two-step generation process, that is, documents are observed as mixtures 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** …

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 …

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

We present a novel method for discovering parallel sentences in comparable, non-parallel
corpora. We train a maximum entropy classifier that, given a pair of sentences, can reliably …

We propose a simple yet effective approach to learning bilingual word embeddings (BWEs)
from non-parallel document-aligned data (based on the omnipresent skip-gram model), and …

Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to
bridge the lexical gap between different languages. In this work, we propose a robust and …

We present a novel method for extracting parallel sub-sentential fragments from
comparable, non-parallel bilingual corpora. By analyzing potentially similar sentence pairs …

We propose a new model for learning bilingual word representations from nonparallel
document-aligned data. Following the recent advances in word representation learning, our …

Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent,
relies on calculating cross-lingual word representations. Inspired by the global paradigm …