In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers. Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots. By using neuro-symbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition. In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge. The meta-knowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots. When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.

This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released Larson dataset, an error of about 9.9% has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.