Abstract: A system and method for training and refining a machine learning model is disclosed. The disclosed system and method can further improve the accuracy of trained machine learning models by calculating which threshold values for predictions (e.g., probabilities output by the machine learning model) provide the most accurate results. The system and method may include applying an optimization technique (e.g., multi-objective optimization) to calculate which threshold values result in the best combination of precision and recall. In other words, the system and method adjust threshold values for prediction scores to optimize the objects of precision and recall. A machine learning model trained with these adjusted threshold values can determine when an input belongs to an unknown class because the unknown input has prediction scores below the threshold values for every known class. Embodiments may include refining an intent classifier to better classify unknown intents.

Abstract: Techniques for building knowledge graphs from conversational data are disclosed. The systems include a high-performance relation classifier developed with active learning and requiring minimal supervision. The classifier is used to classify relation triples extracted from conversational text, which are then used to populate the knowledge graph. A heuristic for constructing the knowledge graph is also disclosed. The proposed embodiments provide a way to efficiently build and/or augment knowledge graphs and improve the quality of the generated responses by a dialogue agent despite a sparsity of data.