Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT
This work addresses slot tagging accuracy for task-oriented dialogue systems, but it is incremental as it builds on existing joint intent and slot detection models.
The paper tackled the problem of slot tagging in task-oriented natural language understanding by leveraging intent label features, resulting in improved performance over state-of-the-art models on benchmark datasets like SNIPS and ATIS and a private Bixby dataset.
Recent joint intent detection and slot tagging models have seen improved performance when compared to individual models. In many real-world datasets, the slot labels and values have a strong correlation with their intent labels. In such cases, the intent label information may act as a useful feature to the slot tagging model. In this paper, we examine the effect of leveraging intent label features through 3 techniques in the slot tagging task of joint intent and slot detection models. We evaluate our techniques on benchmark spoken language datasets SNIPS and ATIS, as well as over a large private Bixby dataset and observe an improved slot-tagging performance over state-of-the-art models.