Multi-agent Learning for Neural Machine Translation
Table 2: BLEU score for the representative models in multi-agent training on NIST Chinese-English translation. X×Y stands for Y agents with the indentical model X but from different initializing seeds.
['MODEL', 'MT02', 'MT03', 'MT04', 'MT08', 'AVERAGE', 'IMPROVEMENTS']
Study on different numbers of agents. We first assess the impact of diverse agents on translation quality. The four baseline systems with different implementations present diverse translation quality, in particular Rel with refined position encoding achieves the best performance. 2) The improvement of each agent after multi-agent learning is dependent on the performance of the co-trained agent (+0.33, +0.8, +0.63 improvements obtained by L2R when training with R2L, Enc and Rel). 3) Better results can be obtained by increasing the number of training agents (e.g., 47.3 → 47.73 → 47.95 for L2R). Study on uniform agents. To measure the importance of diversity in multi-agents, we conduct multiple experiments by initializing the similar model with different initialization seeds to generate multiple agents. Moreover, when training with more than two agents, the performance of d × 3 (47.57) is even lower than arbitrary diverse counterparts (a+b+c (47.74); a+b+d (47.73)).