A sequence-to-sequence approach for numerical slot-filling dialog systems

Abstract

Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the number of guests slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequenceto- sequence (seq2seq) neural model, it is possible to achieve further performance improvement on growing dialog logs in the future.