论文标题
提示词驱动的神经反应产生，词汇不断
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
论文作者
论文摘要
开放域响应生成是为源句子生成明智且内容丰富的重新发起的任务。但是，神经模型倾向于产生安全且无能的反应。尽管提示词引入方法鼓励使用具体语义的响应并显示出巨大的潜力，但它们在解码过程中仍然无法探索di-verse响应。在本文中，我们提出了一种新颖但自然的方法，可以在解码过程中产生多个提示字，然后使用产生的提示字来驱动解码并收缩解码词汇。因此，神经属模型可以探索响应的完整空间，并以效率发现信息。实验结果表明，我们的方法显着优于几个强大的基线模型，其解码复杂性要低得多。特别是，我们的方法可以在解码过程中更有效地收敛到具体语义。
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence. However, neural models tend to generate safe and mean-ingless responses. While cue-word introducing approaches encourage responses with concrete semantics and have shown tremendous potential, they still fail to explore di-verse responses during decoding. In this paper, we propose a novel but natural approach that can produce multiple cue-words during decoding, and then uses the produced cue-words to drive decoding and shrinks the decoding vocabulary. Thus the neural genera-tion model can explore the full space of responses and discover informative ones with efficiency. Experimental results show that our approach significantly outperforms several strong baseline models with much lower decoding complexity. Especially, our approach can converge to concrete semantics more efficiently during decoding.
