  Neural models trained for next utterance generation in dialogue task learn to
mimic the n-gram sequences in the training set with training objectives like
negative log-likelihood (NLL) or cross-entropy. Such commonly used training
objectives do not foster generating alternate responses to a context. But, the
effects of minimizing an alternate training objective that fosters a model to
generate alternate response and score it on semantic similarity has not been
well studied. We hypothesize that a language generation model can improve on
its diversity by learning to generate alternate text during training and
minimizing a semantic loss as an auxiliary objective. We explore this idea on
two different sized data sets on the task of next utterance generation in goal
oriented dialogues. We make two observations (1) minimizing a semantic
objective improved diversity in responses in the smaller data set (Frames) but
only as-good-as minimizing the NLL in the larger data set (MultiWoZ) (2) large
language model embeddings can be more useful as a semantic loss objective than
as initialization for token embeddings.
