Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.

翻译：联合意图检测与槽填充，也称为联合自然语言理解，在智能语音助手中具有重要价值。近年来，该领域的研究主要侧重于利用各种技术提升准确率。可解释性无疑是包括联合自然语言理解模型在内的深度学习模型的重要方面。缺乏可解释性时，模型的决策对外界而言是不透明的，因此有损用户信任。为弥补这一差距，我们将完整的联合自然语言理解模型转化为在细粒度上具有“本质”可解释性，且不牺牲准确率。此外，在实现完整联合自然语言理解模型的可解释性后，我们证明该扩展可成功应用于其他通用分类任务，并通过情感分析和命名实体识别加以验证。