论文标题
基于CVAE的响应产生的焦点受到的注意机制
Focus-Constrained Attention Mechanism for CVAE-based Response Generation
论文作者
论文摘要
为了建模给定文章的多种响应，一种有前途的方法是将潜在变量引入SEQ2SEQ模型。潜在变量应该捕获话语级别的信息并鼓励目标响应的信息。但是，这样的话语级信息通常太粗糙了，无法使用解码器。为了解决这个问题，我们的想法是将粗粒的话语级信息转换为细粒度的单词级信息。具体而言，我们首先通过引入细粒焦点信号来测量帖子单词上相应目标响应的语义浓度。然后，我们提出了一种焦点受限的注意机制，以充分利用焦点，以使输入与目标响应保持一致。实验结果表明，通过利用细粒信号，与几种最先进的模型相比，我们的模型可以产生更多样化和信息性的响应。
To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models. The latent variable is supposed to capture the discourse-level information and encourage the informativeness of target responses. However, such discourse-level information is often too coarse for the decoder to be utilized. To tackle it, our idea is to transform the coarse-grained discourse-level information into fine-grained word-level information. Specifically, we firstly measure the semantic concentration of corresponding target response on the post words by introducing a fine-grained focus signal. Then, we propose a focus-constrained attention mechanism to take full advantage of focus in well aligning the input to the target response. The experimental results demonstrate that by exploiting the fine-grained signal, our model can generate more diverse and informative responses compared with several state-of-the-art models.
