Abstract
Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance.
Highlights
Introduction and Related WorkRecent developments in deep learning have made neural networks the mainstream approach for a wide variety of tasks, ranging from image recognition to price forecasting to natural language processing
Other services do not support any of these languages other than English, but Microsoft Language Understanding Intelligent Service (LUIS) does recommend translating the utterances via their machine translation application programming interface (API) before using the intent detection module
We can see that the normalization of the text improves the intent detection accuracy
Summary
Recent developments in deep learning have made neural networks the mainstream approach for a wide variety of tasks, ranging from image recognition to price forecasting to natural language processing. Neural networks are used for speech recognition and generation, machine translation, text classification, named entity recognition, text generation, and many other tasks. Intent detection is formulated as a classification task; since the dialogue system is created to answer a limited range of questions, there is a finite predefined set of intents. Pattern-based recognition was commonly used before the spread of neural network-based techniques and is a working solution [10,11,12,13], this approach is somewhat limited, and requires labor-intensive creation of a large number of patterns by hand
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