Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Table 6: Bilingual semantic specialisation for: a) Hebrew and Croatian; and b) the original SimLex languages. Each row shows how SimLex scores for that language improve when its distributional vectors are tied into bilingual vector spaces with the four high-resource languages.
['[EMPTY]', '[BOLD] Distrib.', '[BOLD] + EN', '[BOLD] + DE', '[BOLD] + IT', '[BOLD] + RU']
The previous experiment indicates that bilingual specialisation further improves the (already) high-quality estimates for high-resource languages. However, it does little to show how much (or if) the word vectors of lower-resource languages improve during such specialisation. Tying the distributional vectors for these languages (which have no monolingual constraints) into cross-lingual spaces with high-resource ones (which do, in our case from PPDB) leads to substantial improvements. Hebrew and Croatian exhibit similar trends to the original SimLex languages: tying to English and Italian leads to stronger gains than tying to the morphologically sophisticated German and Russian. Indeed, tying to English consistently lead to strongest performance. We believe this shows that bilingual Attract-Repel specialisation with English promises to produce high-quality vector spaces for many lower-resource languages which have coverage among the 271 BabelNet languages (but are not available in PPDB).