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Despite recent progress in generative open-domain dialogue, the issue of lowresponse diversity persists. Prior works have addressed this issue via eithernovel objective functions, alternative learning approaches such as variationalframeworks, or architectural extensions such as the Randomized Link (RL)Transformer. However, these approaches typically entail either additionaldifficulties during training/inference, or a significant increase in model sizeand complexity. Hence, we propose the \underline{Pa}rtially\underline{Ra}ndomized trans\underline{Former} (PaRaFormer), a simple extensionof the transformer which involves freezing the weights of selected layers afterrandom initialization. Experimental results reveal that the performance of thePaRaformer is comparable to that of the aforementioned approaches, despite notentailing any additional training difficulty or increase in model complexity.

Partially Randomizing Transformer Weights for Dialogue Response Diversity