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.

将完全的联合 NLU 模型在粒度级别上‘本质地’可解释化，以提高准确性，并在情感分析和命名实体识别等其他广义分类任务中成功应用。

可解释准确的自然语言理解：为语音助手及其它应用而设计

Explainable and Accurate Natural Language Understanding for Voice  Assistants and Beyond

Joint intent detection and slot filling is a key research topic in natural
language understanding (NLU). Existing joint intent and slot filling systems
analyze and compute features collectively for all slot types, and importantly,
have no way to explain the slot filling model decisions. In this work, we
propose a novel approach that: (i) learns to generate additional slot type
specific features in order to improve accuracy and (ii) provides explanations
for slot filling decisions for the first time in a joint NLU model. We perform
an additional constrained supervision using a set of binary classifiers for the
slot type specific feature learning, thus ensuring appropriate attention
weights are learned in the process to explain slot filling decisions for
utterances. Our model is inherently explainable and does not need any post-hoc
processing. We evaluate our approach on two widely used datasets and show
accuracy improvements. Moreover, a detailed analysis is also provided for the
exclusive slot explainability.

本研究提出了一种新颖的基于监督学习的方法，它可以学习生成针对不同槽类型的特征以提高准确性，并提供了首个能够解释基于联合 NLU 模型的槽填充决策的方法，从而保证了模型的可解释性。实验结果表明，该方法在两个广泛使用的数据集上均取得了准确性改进，并提供了有关独占式槽解释性的详细分析。