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 net-work is trained to minimize perplexity, an automatic metric that we compare against human judgement of multi-turn conversation quality.To capture this judgement, we propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of good conversation. Interestingly, 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 existing chatbots that we evaluated. Read More

#chatbots

Chatbots are among the most trending technology for which the industry is excited to get in integrated. They get touted as the next rendition of applications, similar to an immense change in the correspondence business. Since Facebook has extended access to its messenger administration, it is enabling firms to achieve clients better through various APIs. Read More

#chatbots