Although neural models have achieved competitive results in dialogue systems,
they have shown limited ability in representing core semantics, such as
ignoring important entities. To this end, we exploit Abstract Meaning
Representation (AMR) to help dialogue modeling. Compared with the textual
input, AMR explicitly provides core semantic knowledge and reduces data
sparsity. We develop an algorithm to construct dialogue-level AMR graphs from
sentence-level AMRs and explore two ways to incorporate AMRs into dialogue
systems. Experimental results on both dialogue understanding and response
generation tasks show the superiority of our model. To our knowledge, we are
the first to leverage a formal semantic representation into neural dialogue
modeling.

我们使用抽象意义表示（AMR）来协助神经对话建模，将 AMR 图形构建到对话系统中会更显著地提高对话理解和响应生成任务的效果。

对话建模的语义表示

Semantic Representation for Dialogue Modeling

We investigate the impact of search strategies in neural dialogue modeling.
We first compare two standard search algorithms, greedy and beam search, as
well as our newly proposed iterative beam search which produces a more diverse
set of candidate responses. We evaluate these strategies in realistic full
conversations with humans and propose a model-based Bayesian calibration to
address annotator bias. These conversations are analyzed using two automatic
metrics: log-probabilities assigned by the model and utterance diversity. Our
experiments reveal that better search algorithms lead to higher rated
conversations. However, finding the optimal selection mechanism to choose from
a more diverse set of candidates is still an open question.

本文研究了搜索策略在神经对话建模中的影响，比较了两种标准搜索算法以及新提出的迭代型 beam 搜索算法。通过模型基础贝叶斯校准来解决注释器 Bias 的问题，并通过模型分配的对数概率和话语多样性这两种自动度量标准来分析对话内容。研究发现更好的搜索算法能够带来更受欢迎的对话，但选择适当的候选人选机制仍需进一步研究。