Emergent Translation in Multi-Agent Communication
Table 1: Test BLEU scores for each model and dataset. The best performing (unaligned) model for each dataset is shown in bold. We show the results of baselines from (Nakayama & Nishida, 2017) using two different loss functions (Appendix A). Pretrained denotes initializing the speaker modules and image encoders with pretrained image captioning models. Fixed denotes fixing the parameters of either the speaker module or the image encoder.
['[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', 'Multi30k Task 1 EN-DE', 'Multi30k Task 1 DE-EN', 'Multi30k Task 2 EN-DE', 'Multi30k Task 2 DE-EN', 'COCO & STAIR EN-JA', 'COCO & STAIR JA-EN']
S5SS0SSS0Px6 Results We find that naïvely looking up the nearest training image and retrieving its captions gives relatively poor BLEU scores ( On the other hand, training an NMT model on these visually closest neighbor pairs gives much better translation performance.