Towards Decoding as Continuous Optimisation in Neural Machine Translation
Table 2: The BLEU evaluation results with EG algorithm against 100-best reranking on WMT evaluation dataset; †: best performance.
['[EMPTY]', 'BLEU', 'AvgLen']
S5SS2SSS0Px4 EG vs Reranking. Reranking is an alternative method for integrating global factors into the existing NMT systems. We compare our EG decoding algorithm against the reranking approach with bidirectional factor where the N-best outputs of a left-to-right decoder is re-scored with the forced decoder operateing in a right-to-left fashion. Our EG algorithm initialised with the reranked output achieves the best BLEU score. We also compare reranking with EG algorithm initialised with the beam decoder, where for direct comparison we filter out sentences with length greater than that of the beam output in the k-best lists. These results show that the EG algorithm is capable of effectively exploiting the search space.