Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Table 8: NBT model accuracy across the three languages. Each figure shows the performance of the model trained using the subspace of the given vector space corresponding to the target language. For the English baseline figures, we show the stronger of the EN-IT / EN-DE figures.
['[BOLD] Word Vector Space', '[BOLD] EN', '[BOLD] IT', '[BOLD] DE']
Intrinsic vs. Downstream Evaluation If mono- and cross-lingual semantic specialisation improves the semantic content of word vector collections according to intrinsic evaluation, we would expect the NBT model to perform higher-quality belief tracking when such improved vectors are deployed. We investigate the difference in DST performance for English, German and Italian when the NBT model employs the following word vector collections: 1) distributional word vectors; 2) monolingual semantically specialised vectors; and 3) monolingual subspaces of the cross-lingual semantically specialised EN-DE-IT-RU vectors. For each language, we also compare to the NBT performance achieved using the five state-of-the-art bilingual vector spaces we compared to in Sect. 5.3.