Open-domain chatbots are more generic dialogue systems. An example is the Poly-encoder from BIBREF7 humeau2019real. It outperforms the Bi-encoder BIBREF8, BIBREF9 and matches the performance of the Cross-encoder BIBREF10, BIBREF11 while maintaining reasonable computation time. It performs strongly on downstream language understanding tasks involving pairwise comparisons, and demonstrates state-of-the-art results on the ConvAI2 challenge BIBREF12. Feed Yourself BIBREF13 is an open-domain dialogue agent with a self-feeding model. When the conversation goes well, the dialogue becomes part of the training data, and when the conversation does not, the agent asks for feedback. Lastly, Kvmemnn BIBREF14 is a key-value memory network with a knowledge base that uses a key-value retrieval mechanism to train over multiple domains simultaneously. We use all three of these models as baselines for comparison.  We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20. 
 Question: What baseline models are used?
the Poly-encoder from BIBREF7 humeau2019real Feed Yourself BIBREF13 is an open-domain dialogue agent with a self-feeding model Kvmemnn BIBREF14 is a key-value memory network with a knowledge base that uses a key-value retrieval mechanism to train over multiple domains simultaneously We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi-ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate scheduler and length capping settings) described in Section SECREF20. a BERT bi-ranker