Bilingual Data Selection Using a Continuous Vector-Space Representation
Data selection aims to select the best data subset from an available pool of sentences with which to train a pattern recognition system. In this article, we present a bilingual data selection method that leverages a continuous vector-space representation of word sequences for selecting the best subset of a bilingual corpus, for the application of training a machine translation system. We compared our proposal with a state-of-the-art data selection technique (cross-entropy) obtaining very promising results, which were coherent across different language pairs.
Abstract This study contributes to the literature on socially responsible investing by examining the diversification potential of commodities, specifically oil, gold and clean energy together with the Brazilian Corporate Sustainability Index (ISE). Multivariate GARCH models are used to model volatility spillovers and conditional correlation in pairs of stocks containing ISE. Specifically, A-BEKK and A-DCC models with spillovers are estimated. The models’ results are used to compute and analyze the optimal weights and hedge ratios for stock portfolio holdings. The greatest benefit from diversification is obtained through the acquisition of gold and then OVX.