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The goal of this paper is to report certain scientific discoveries about aSeq2Seq model. It is known that analyzing the behavior of RNN-based models atthe neuron level is considered a more challenging task than analyzing a DNN orCNN models due to their recursive mechanism in nature. This paper aims toprovide neuron-level analysis to explain why a vanilla GRU-based Seq2Seq modelwithout attention can achieve token-positioning. We found four different typesof neurons: storing, counting, triggering, and outputting and further uncoverthe mechanism for these neurons to work together in order to produce the righttoken in the right position.

Exposing the Functionalities of Neurons for Gated Recurrent Unit Based Sequence-to-Sequence Model