Natural Language Understanding (NLU) systems are essential components in many industry conversational artificial intelligence applications. There are strong incentives to develop a good NLU capability in such systems, both to improve the user experience and in the case of regulated industries for compliance reasons. We report on a series of experiments comparing the effects of optimizing word embeddings versus implementing a multi-classifier ensemble approach and conclude that in our case, only the latter approach leads to significant improvements. The study provides a high-level primer for developing NLU systems in regulated domains, as well as providing a specific baseline accuracy for evaluating NLU systems for financial guidance.
Jenset, G. B. and McGillivray, B. (2019). Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model. Machine Learning and Knowledge Extraction. 2019, 1(2), 630-640 https://doi.org/10.3390/make1020037