  Sequence-to-sequence models have been applied to the conversation response
generation problem where the source sequence is the conversation history and
the target sequence is the response. Unlike translation, conversation
responding is inherently creative. The generation of long, informative,
coherent, and diverse responses remains a hard task. In this work, we focus on
the single turn setting. We add self-attention to the decoder to maintain
coherence in longer responses, and we propose a practical approach, called the
glimpse-model, for scaling to large datasets. We introduce a stochastic
beam-search algorithm with segment-by-segment reranking which lets us inject
diversity earlier in the generation process. We trained on a combined data set
of over 2.3B conversation messages mined from the web. In human evaluation
studies, our method produces longer responses overall, with a higher proportion
rated as acceptable and excellent as length increases, compared to baseline
sequence-to-sequence models with explicit length-promotion. A back-off strategy
produces better responses overall, in the full spectrum of lengths.
