Improving Bilingual Projections via Sparse Covariance Matrices

Mapping documents into an interlingual representation can help bridge the language barrier of cross-lingual corpora. Many existing approaches are based on word co-occurrences extracted from aligned training data, represented as a covariance matrix. In theory, such a covariance matrix should represent semantic equivalence, and should be highly sparse. Unfortunately, the presence of noise leads to dense covariance matrices which in turn leads to suboptimal document representations. In this paper, we explore techniques to recover the desired sparsity in covariance matrices in two ways. First, we explore word association measures and bilingual dictionaries to weigh the word pairs. Later, we explore different selection strategies to remove the noisy pairs based on the association scores. Our experimental results on the task of aligning comparable documents shows the efficacy of sparse covariance matrices on two data sets from two different language pairs.