Despite the recent success of deep transfer learning approaches in NLP, there is a lack of quantitative studies demonstrating the gains these models offer in low-shot text classification tasks over existing paradigms. Deep transfer learning approaches such as BERT and ULMFiT demonstrate that they can beat state-of-the-art results on larger datasets, however when one has only 100-1000 labelled examples per class, the choice of approach is less clear, with classical machine learning and deep transfer learning representing valid options. This paper compares the current best transfer learning approach with top classical machine learning approaches on a trinary sentiment classification task to assess the best paradigm. We find that BERT, representing the best of deep transfer learning, is the best performing approach, outperforming top classical machine learning algorithms by 9.7% on average when trained with 100 examples per class, narrowing to 1.8% at 1000 labels per class. We also show the robustness of deep transfer learning in moving across domains, where the maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross domain, compared to classical machine learning which loses up to 20.6%.
The allure of foreign markets gleams ever brighter in the turbulent market conditions facing the ordinary South African investor. Considering that since December 2015, the JSE All Share Index had lost 4.22% (at the time of writing) and dropped 10.19% at its worst point, and that the rand had lost 10.93% against the US dollar (although it has since staged a bit of a recovery), investors may look to diversify their investments internationally and invest in a more stable currency as an inflation hedge.
Despite the recent success of deep transfer learning approaches in NLP, there is a lack of quantitative studies demonstrating the gains these models offer in low-shot text classification tasks over existing paradigms. Deep transfer learning approaches such as BERT and ULMFiT demonstrate that they can beat state-of-the-art results on larger datasets, however when one has only 100-1000 labelled examples per class, the choice of approach is less clear, with classical machine learning and deep transfer learning representing valid options. This paper compares the current best transfer learning approach with top classical machine learning approaches on a trinary sentiment classification task to assess the best paradigm. We find that BERT, representing the best of deep transfer learning, is the best performing approach, outperforming top classical machine learning algorithms by 9.7% on average when trained with 100 examples per class, narrowing to 1.8% at 1000 labels per class. We also show the robustness of deep transfer learning in moving across domains, where the maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross domain, compared to classical machine learning which loses up to 20.6%.
This study has foci on the global drivers of currencies and their relationship to economic jurisdiction in the presence of global risk appetite. We focus on a comprehensive basket of global currencies, deriving three statistically motivated currency market factors. We cluster on these factor loadings and find three currency groupings – Developed/European, Emerging/Commodity as well as an Asian currency cluster. Constructing ‘risk states’ based on the VIX Volatility Index, we find that Developed/European and Emerging/Commodity cluster currencies are better explained by the first three principal components when in the ‘Low’ and ‘High’ risk states respectively. As previously corroborated in the literature, we find evidence that the second principal component is a ‘carry trade factor’. We detail that the impact of this factor on currencies in the Emerging/Commodity cluster is heightened (for both positive and negative changes) by ‘High’ risk states.
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