Title: Semi-Supervised Heterogeneous Feature Learning in a Large-Scale Conversational AI System
Abstract: This paper aims to improve an important downstream component of a large-scale industrial conversational AI system. The component is called the Skill Routing Component (SRC) and is responsible for a variety of tasks. As the last component before executing user requests, SRC utilizes many textual and symbolic features obtained from heterogeneous upstream components like automatic speech recognition (ASR) and natural language understanding (NLU), which necessitates the need for an efficient way to utilize these features. To achieve this, we propose a unified transformer model which in contrast to the traditional methods encodes the heterogeneous features into a shared latent space. Next, there is an inherent connection between SRC tasks and upstream NLU tasks. We utilize noisy NLU data for pre-training the unified SRC model via specifically curated objectives and fine-tune it separately on the different SRC tasks. Our method shows an average improvement of 1.8% on four SRC tasks over the state-of-the-art baseline.