The semantic understanding of natural dialogues composes of several parts. Some of them, like intent classification and entity detection, have a crucial role in deciding the next steps in handling user input. Handling each task as an individual problem can be wasting of training resources, and also each problem can benefit from each other. This paper tackles these problems as one. Our new model, which combine intent and entity recognition into one system, is achieving better metrics in both tasks with lower training requirements than solving each task separately. We also optimize the model based on the inputs.
翻译:对自然对话的语义理解由几个部分组成,其中一些部分,如意图分类和实体探测,在决定处理用户投入的下一步步骤方面起着关键作用。将每项任务作为单个问题处理可能会浪费培训资源,而且每个问题都可以从中得益。本文件将这些问题作为一个问题处理。我们的新模式将意图和实体的承认结合到一个系统中,在两项任务中都取得了比单独解决每项任务更低的培训要求更好的衡量标准。我们还根据投入优化了模式。