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.

In this large study of GDM in Chinese women, advanced maternal age, pre-pregnancy overweight or obesity and family history of diabetes were confirmed to be risk factors. In addition, a history of recurrent vulvovaginal candidiasis or spontaneous abortion and residency in south China appeared to be novel risk factors in this population.

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The association between AFLP and the E474Q mutation in the fetus is significant. Screening newborns for this mutation in pregnancies complicated by AFLP could allow early diagnosis and treatment in newborns and genetic counseling and prenatal diagnosis in subsequent pregnancies in affected families.

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