Abstract: A system is provided including a processor and a non-transitory computer-readable medium storing computing instructions that cause the processor to perform: generating, by a pretrained bidirectional encoder representations from transformers (BERT) model, a query embedding vector; generating, by a preprocessor, a context vector; receiving, by an attention component, the query embedding and the context vectors; generating, by an attention component, an attention vector based on the query embedding and the context vectors; generating an attention-weighted context vector by multiplying the attention and context vectors; generating a combined embedding by concatenating the attention-weighted context and query embedding vectors; receiving, by a multi-layer perceptron (MLP) of a context-aware natural language understanding (NLU) model, at least one of the combined embedding or the attention-weighted context vector; and generating, by the MLP, at least one predicted intent of the user based on at least one of the com

Abstract: System and methods for generating a cross-domain multilingual model are disclosed. In some embodiments, a disclosed method includes: storing, in a database, a plurality of first utterances associated with a first language, training a first model using the plurality of first utterances, the first model being associated with the first language, generating, using the first model, a plurality of first representations associated with the plurality of first utterances, training a second model, using the plurality of first representations, the second model being associated with a plurality of second languages, receiving, using the second model, a second utterance in the second language, and generating, using the second model, a response in one or more languages of the plurality of second languages.

Abstract: Systems and methods for automated named entity recognition (NER) using artificial intelligence models are disclosed. In some examples, a contextualized word embedding is generated for each of a plurality of words. Further, for each contextualized word embedding, example contextualized word embeddings are received. Each of the example contextualized word embeddings are associated with a corresponding digital textual example. A similarity value is generated between each contextualized word embedding and each of the corresponding example contextualized word embeddings. Based on the similarity values, one or more of the contextualized word embeddings are determined. An input prompt is generated that includes a command, the plurality of words, and the digital textual example associated with each of the determined contextualized word embeddings.

Abstract: A system for determining more accurate labels for nodes in a graph. The system includes an electronic computing device. The electronic computing device includes an electronic processor. The electronic processor is configured to receive a graph including a plurality of nodes linked by one or more connections. The electronic processor is also configured to augment the graph by creating one or more new connections in the graph. For each node of the plurality of nodes included in the augmented graph, the electronic processor is configured to, using a first machine learning model, determine a first vector associated with the node based on the augmented graph, using a second machine learning model, determine a second vector associated with the node based on the augmented graph, and determine the more accurate label for the node based on the first vector and the second vector.

Abstract: The disclosure relates to methods and systems of partitioning-based scalable weighted aggregation composition for embeddings learned from knowledge graphs for training neural networks to perform downstream machine-learning tasks. For example, a system may access a knowledge graph comprising a plurality of nodes and partition the knowledge graph into a plurality of partitions based on edge densities between nodes of the knowledge graph. The system may perform partition-wise encoding using compositional message passing between nodes that enables learning from neighboring nodes. The system may generate an embedding for each node and each relation type in each partition based on the partition-wise encoding using compositional message passing. The system may concatenate the generated embeddings from the plurality of partitions. The system may train a global neural network for a downstream prediction task based on the concatenated embeddings using one or more weight matrices.