论文标题
解决实体选择的间接推荐表达式
Resolving Indirect Referring Expressions for Entity Selection
论文作者
论文摘要
语言建模的最新进展已实现了新的对话系统。特别是，人们通常希望在使用此类系统时在指定的选项中做出选择。当人们使用自然表达式在实体之间进行选择时，我们将解决这个参考分辨率的问题。例如，考虑到“我们应该做一个simnel蛋糕还是潘丹蛋糕？”对话参与者的自然反应可能是间接的：“让我们成为绿色的人”。这样的天然表达很少研究以解决参考分辨率。我们认为，强烈理解这种语言具有改善对话，建议和搜索系统的自然性的巨大潜力。我们创建了Altentities（替代实体），这是42K实体对和表达式的新公共数据集（指配对中的一个实体），并为歧义问题开发模型。我们的语料库首次可以研究如何适应该任务的语言模型，包括跨三个领域的间接参考表达式组成。我们发现它们在现实环境中实现了82％-87％的精度，尽管合理也邀请了进一步的进步。
Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address this problem of reference resolution, when people use natural expressions to choose between the entities. For example, given the choice `Should we make a Simnel cake or a Pandan cake?' a natural response from a dialog participant may be indirect: `let's make the green one'. Such natural expressions have been little studied for reference resolution. We argue that robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems. We create AltEntities (Alternative Entities), a new public dataset of 42K entity pairs and expressions (referring to one entity in the pair), and develop models for the disambiguation problem. Consisting of indirect referring expressions across three domains, our corpus enables for the first time the study of how language models can be adapted to this task. We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances.
