Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage
Table 2: SxS performance of PLuGS vs. TGT models (upper half) and PLuGS vs. TTG models (lower half), across five target languages on OID1k. The PLuGS models perform better on both GainSxS and GainAccept metrics, for all five languages.
['[BOLD] Lang', '[ITALIC] Wins', '[ITALIC] Losses', '[ITALIC] Gainsxs', 'PLuGS [ITALIC] Accept', 'TGT [ITALIC] Accept', '[ITALIC] GainAccept']
In this section, we evaluate the performance of models trained using Conceptual Captions, as detailed in Sec. The results show that, for all five languages, the PLuGS model captions are consistently superior to the TGT captions on both GainSxS and GainAccept metrics. The GainSxS are between 3% and 5% absolute percentages between TGT and PLuGS models, and 1% and 3% absolute percentages between TTG and PLuGS models, with similar trends for the GainAccept metric.