  Deep learning methods have recently achieved great empirical success on
machine translation, dialogue response generation, summarization, and other
text generation tasks. At a high level, the technique has been to train
end-to-end neural network models consisting of an encoder model to produce a
hidden representation of the source text, followed by a decoder model to
generate the target. While such models have significantly fewer pieces than
earlier systems, significant tuning is still required to achieve good
performance. For text generation models in particular, the decoder can behave
in undesired ways, such as by generating truncated or repetitive outputs,
outputting bland and generic responses, or in some cases producing
ungrammatical gibberish. This paper is intended as a practical guide for
resolving such undesired behavior in text generation models, with the aim of
helping enable real-world applications.
