Abstract: Aspects of the present disclosure provide techniques for FAQ retrieval. Embodiments include receiving, via a user interface of a computing application, a query related to a subject. Embodiments include generating a first multi-dimensional representation of the query. Embodiments include obtaining a plurality of question and answer pairs related to the subject and, for a given question and answer pair comprising a given question and a given answer, generating a second multi-dimensional representation of the given question and a third multi-dimensional representation of the given answer. Embodiments include providing input to a model based on the first multi-dimensional representation, the second multi-dimensional representation, and the third multi-dimensional representation and determining a match score for the query and the given question and answer pair based on an output of the model.

Abstract: This disclosure relates to extracting entities from unstructured text. The unstructured text is segmented into structured segments with one or more instances, that belong to different topics, with a topic segmentation model. Each instances of the structured segment is operated on by an entity extraction model to extract entities, and the extracted entities associated with each topic is produced in a computer-readable format. The relations between extracted entities associated with each topic may be identified.

Abstract: Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

Abstract: A method for facilitating user support using multimodal information involves obtaining an interaction between a user and a support agent, generating a question embedding from the interaction, obtaining a clickstream associated with the interaction, and generating a clickstream embedding from the clickstream. The question embedding and the clickstream embedding form a shared latent space representation. The method further involves decoding a problem summary from the shared latent space representation and providing the problem summary to the support agent.

Abstract: Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

Abstract: A method may include receiving an unstructured question from a user having structured contextual features. The unstructured question may include tokens. The method may further include converting, using a sentence embedding model, the tokens to a question vector, assigning the question vector to a question cluster, assigning, by applying a user clustering model to the question cluster and the structured contextual features, the user to a user cluster, and assigning, using a trained machine learning model, a channel to the user cluster. The channel may be used to communicate with a customer service agent for a management application. The trained machine learning model may assign, using metrics, a channel to each user cluster. The method may further include recommending, based on assigning the channel to the user cluster, the channel to the user for the question.

Abstract: A computer-implemented method is provided to perform text classification with a neural network system. The method includes providing a computing device to receive input datasets including user input question text and feed the datasets to the neural network system. The neural network system includes one or more neural networks configured to extract and concatenate character-based features, word-based features from the question datasets and clickstream embeddings of clickstream data to form a representation vector indicative of the question text and user behavior. A representation vector is fed into fully connected layers of a feed-forward network. The feed-forward network is configured to predict a first class and a second class associated with respective user input questions based on the representation vector.

Abstract: A method for text classification involves generating, using a bilingual embedding model, source language embeddings for source language documents; obtaining source language document labels of the source language documents; and training a source language classifier model and a label embedding network, executing on a computing system, using the source language embeddings and the source language document labels. The method further involves generating pseudo-labels for unlabeled target language documents, by: generating, using the bilingual embedding model, target language embeddings for the unlabeled target language documents, and applying the source language classifier model and the label embedding network to the target language embeddings to obtain the pseudo-labels for the unlabeled target language documents. In addition, the method involves training a target language classifier model executing on the computing system using the target language embeddings and the pseudo labels.

Abstract: A method for facilitating user support using multimodal information involves obtaining an interaction between a user and a support agent, generating a question embedding from the interaction, obtaining a clickstream associated with the interaction, and generating a clickstream embedding from the clickstream. The question embedding and the clickstream embedding form a shared latent space representation. The method further involves decoding a problem summary from the shared latent space representation and providing the problem summary to the support agent.

Abstract: A computer-implemented method is provided to perform text classification with a neural network system. The method includes providing a computing device to receive input datasets including user input question text and feed the datasets to the neural network system. The neural network system includes one or more neural networks configured to extract and concatenate character-based features, word-based features from the question datasets and clickstream embeddings of clickstream data to form a representation vector indicative of the question text and user behavior. A representation vector is fed into fully connected layers of a feed-forward network. The feed-forward network is configured to predict a first class and a second class associated with respective user input questions based on the representation vector.