Multi-agent Learning for Neural Machine Translation
Table 6: Accuracy of subject-verb agreement (SVA) and word sense disambiguation (WSD) for different models. We report the performance of L2R in different models for comparison of using different agents.
['Models', 'SVA (%)', 'WSD (%)']
The L2R improves its ability of SVA with the help of the other agents significantly. 2) The Enc presents better advantage in resolving WSD than in SVA, while Rel has the opposite preference. 3) Although the R2L presents ordinary BLEU score, it still does help the L2R resolve the SVA.