Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage
Table 6: Performance of Stabilizers used as captions from PLuGS models for three languages vs the captions produced by the baseline English model. The PLuGS Stabilizer outputs are better captions across all three languages.
['[BOLD] Model', '[ITALIC] Wins', '[ITALIC] Losses', '[ITALIC] Gainsxs', '[ITALIC] BAccept', '[ITALIC] AAccept', '[ITALIC] GainAccept']
We perform SxS human evaluations over Stabilizer captions (English) for three different PLuGS-2L models (trained for French, German, and Spanish). The Gainsxs are between 5% and 6% absolute percentage improvements, while GainAccept also improves up to 3.4% absolute for the PLuGS-Fr model.