Leveraging Monolingual Data for Crosslingual Compositional Word Representations
Table 2: Results for our proposed models, baselines, and related work. All results are reported for a training set size of 1,000 documents for each language. We refer to our proposed method as Binclusion.
['Method', 'Training Data', '[ITALIC] EN→ [ITALIC] DE', '[ITALIC] DE→ [ITALIC] EN']
We compare our method to various architectures introduced in previous work. As these methods differ in their ability to handle monolingual data, we evaluate several versions of our model using different data sources and sizes for training. Also, we follow the lines of previous work and use 40-dimensional word representations. We report results when using the first 500,000 sentence pairs of EuroParl (Euro500k), the full EuroParl corpus (EuroFull), the first 500,000 sentence pairs of EuroParl and the German and English text from the Reuters corpus as monolingual data (Euro500kReuters), and one version using the full EuroParl and Reuters corpus (EuroFullReuters). The result table includes previous work as well as the Glossed, the machine translation and the majority class baselines from Klementiev et al.