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.

该文研究应用序列到序列模型解决对话响应生成问题的方法，使用基于注意力机制的解码器保证响应内容连贯性，并提出了一种名为‘glimpse-model’的实用方法以适应大型数据集，并使用随机 beam 搜索算法注入多样性来增加生成可接受响应的比例和整体响应长度。