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Mixture of Softmaxes (MoS) has been shown to be effective at addressing theexpressiveness limitation of Softmax-based models. Despite the known advantage,MoS is practically sealed by its large consumption of memory and computationaltime due to the need of computing multiple Softmaxes. In this work, we set outto unleash the power of MoS in practical applications by investigating improvedword coding schemes, which could effectively reduce the vocabulary size andhence relieve the memory and computation burden. We show both BPE and ourproposed Hybrid-LightRNN lead to improved encoding mechanisms that can halvethe time and memory consumption of MoS without performance losses. With MoS, weachieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-Englishcorpus and an improvement of 0.76 CIDEr score on image captioning. Moreover, onthe larger WMT 2014 machine translation dataset, our MoS-boosted Transformeryields 29.5 BLEU score for English-to-German and 42.1 BLEU score forEnglish-to-French, outperforming the single-Softmax Transformer by 0.8 and 0.4BLEU scores respectively and achieving the state-of-the-art result on WMT 2014English-to-German task.

Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation