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The softmax attention mechanism has emerged as a noteworthy development inthe field of Artificial Intelligence research, building on the successes ofTransformer-based architectures. However, their ever increasing sizesnecessitate ever increasing computational memory, that limits their usage. Wepropose KgV, a sigmoid gating mechanism that, in conjunction with softmaxattention, significantly boosts performance without increasing architecturesize. To amend the size requirements, we leverage Tensor Chains to identify andprune the excess parameters. We find that such excess resides primarily withinthe embedding layer, and not in the output linear layer. To further improveembedding and significantly reduce parameters, we introduce H-SoftPOS, ahierarchical embedding layer which simultaneously enhances performance.Remarkably, on the WMT14 English-German validation set, our approach yields athreefold reduction in perplexity, surpassing the current state-of-the-art,while reducing parameter counts also by a factor of 3. When we further reducethe number of parameters up to sevenfold, we can still achieve a 21\% decreasein perplexity with respect to the baseline Transformer. To understandgeneralization capabilities, we conduct experiments on the 7 language pairs ofthe WMT17 dataset. Our method outperforms existing techniques in terms of testloss while simultaneously halving the number of parameters. Moreover, weobserve a 70 times reduction in variance with respect to the priorstate-of-the-art. In conclusion, our proposed method yields significantimprovements in performance and much lower memory cost. We call the resultingarchitecture Anthe.

Less is More! A slim architecture for optimal language translation