Language classification from bilingual word embedding graphs
Table 1: Correlation coefficients δ=δp,ℓ in % on MTurk-287 for BBA and CCA methods, respectively, for various second languages ℓ. Second languages ordered by semantic similarity to p= English, as determined by Eq. (1); see §4.2 for specifics.
['[EMPTY]', 'BBA', 'CCA']
We notice two trends. First, for BBA, results can roughly be partitioned into three classes. The languages pt, es, fr, it have best performances as second languages with δ values between 54% and close to 60%; the next group consists of da, nl, ro, de, el, bg, sv, sl, cs with values of around 50%; finally, fi, pl, hu, lv, lt, sk, et perform worst as second languages with δ values of around 47%. So, for BBA, the choice of second language has evidently a considerable effect in that there is ∼26% difference in performance between best second language, ℓ= it, and worst second languages, ℓ= pl/sk/et. Moreover, it is apparently better to choose (semantically) similar languages — with reference to the target language p= English — as second language in this case. Secondly, for CCA, variation in results is much less pronounced. For example, the best second languages, et/lv, are just roughly 5.5% better than the worst second language, lt. Moreover, it is not evident, on first view, that performance results depend on language similarity in this case.