  We present Meena, a multi-turn open-domain chatbot trained end-to-end on data
mined and filtered from public domain social media conversations. This 2.6B
parameter neural network is simply trained to minimize perplexity of the next
token. We also propose a human evaluation metric called Sensibleness and
Specificity Average (SSA), which captures key elements of a human-like
multi-turn conversation. Our experiments show strong correlation between
perplexity and SSA. The fact that the best perplexity end-to-end trained Meena
scores high on SSA (72% on multi-turn evaluation) suggests that a human-level
SSA of 86% is potentially within reach if we can better optimize perplexity.
Additionally, the full version of Meena (with a filtering mechanism and tuned
decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots
we evaluated.
