Bridging linguistic typology and multilingual machine translation with multi-view language representations
Table 1: Avg. accuracy (↑) of typological feature prediction per NMT-learned and SVCCA(US,L∗) setting.
['[EMPTY]', 'one-language-out Single', 'one-language-out SVCCA', 'one-family-out #F.', 'one-family-out Single', 'one-family-out SVCCA']
In the case of LB (with 731 entries), we notice that the overall performance drops, and the SVCCA transformation cannot improve it. In conclusion, we notice that specific typological knowledge is usually hard to learn in an unsupervised way, and fusing them with KB vectors using SVCCA is feasible for inducing information of linguistics typology in some scenarios.