Abstract: An IVR and chatbot, or other system, employing a language model, the language model resulting from a method and computer product encoding the method is available for preparing a domain or subdomain specific glossary. The method included using probabilities, word context, common terminology and different terminology to identify domain and subdomain specific language and a related glossary updated according to the method.

Abstract: Certain aspects of the present disclosure provide techniques for generating multivariate time series data utilizing a variational auto-encoder (VAE) having an architecture for injecting custom temporal structures into the generated multivariate time series data. A method for generating multivariate time series data includes sampling a multivariate distribution forming a latent space vector, processing the latent space vector with an interpretable decoder of a variational auto-encoder, an architecture of the interpretable decoder comprising a plurality of blocks including one or more blocks configured to inject one or more temporal structures into multivariate time series data, and outputting, from the interpretable decoder, generated multivariate time series data comprising one or more temporal structures defined by the injected one or more temporal structures.

Abstract: An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.

Abstract: According to principles described herein, unsupervised statistical models, semi-supervised data models, and HITL methods are combined to create a text normalization system that is both robust and trainable with a minimum of human intervention.

Abstract: Disclosed is a measure to collect and aggregate performance, sales, call center, custom KPI, and WA data in order to construct a model of correlations and causations between metrics to provide a means to measure associated costs of improving performance metrics for use in an interactive planning model. The interactive planning model allows for allocating budget across performance metrics, leveraging correlations and multiple cost models. The information is provided via a dashboard of IVA performance metrics and allows for comparison of real time IVA performance metrics to target metrics set by the IVA owner and incorporating KPIs provided by the WA owners.

Abstract: This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.

Abstract: Systems and methods are provided to combine data from various internal data sources and external data sources to generate and maintain frequently asked questions and knowledge bases. By doing so, the collective knowledge of a company and customers' questions around it are aggregated and served in a way that increases the availability and relevancy of such knowledge for both self-help channels and computing agents (e.g., customer service representatives (CSRs)).

Abstract: An external content engine automatically monitors content items generated by external data sources such as online merchants, social networking platforms, and discussion forums for an entity. The monitored content items may include public messages such as posts, reviews, and comments. When a content item is identified that references or relates to the entity, natural language processing is used to determine if the content item has a positive or negative context. The external content engine may then determine an action to take based on the context and other factors such as a popularity or influence of the author of the content item.

Abstract: As described herein, a system for expanding contractions in electronically stored text includes expanding contractions having only on expanded form. For remaining contractions, a grammar check is performed for all possible expanded forms to determine if an expanded form can be selected based on context and grammar rules. If an expanded form is not evident from the first two steps, all possible expanded forms of the remaining contractions are converted to a vector representation along with the original text. A Word Movers Distance (WMD) for each possible expansion is calculated using the vectors for each possible expansion and the original text. An expanded form is chosen without human intervention based on the grammar score alone or the WMD and the grammar score.

Abstract: A scalable system provides automated conversation review that can identify potential miscommunications. The system may provide suggested actions to fix errors in intelligent virtual assistant (IVA) understanding, may prioritize areas of language model repair, and may automate the review of conversations. By the use of an automated system for conversation review, problematic interactions can be surfaced without exposing the entire set of conversation logs to human reviewers, thereby minimizing privacy invasion. A scalable system processes conversations and autonomously marks the interactions where the IVA is misunderstanding the user.

Abstract: This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.

Abstract: An IVR and chatbot, or other system, employing a language model, the language model resulting from a method and computer product encoding the method is available for preparing a domain or subdomain specific glossary. The method included using probabilities, word context, common terminology and different terminology to identify domain and subdomain specific language and a related glossary updated according to the method.

Abstract: A system and method for updating computerized language models is provided that automatically adds or deletes terms from the language model to capture trending events or products, while maximizing computer efficiencies by deleting terms that are no longer trending and use of knowledge bases, machine learning model training and evaluation corpora, analysis tools and databases.

Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.

Abstract: To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.

Abstract: Disclosed is a system and method for detecting and addressing bias in training data prior to building language models based on the training data. Accordingly system and method, detect bias in training data for Intelligent Virtual Assistant (IVA) understanding and highlight any found. Suggestions for reducing or eliminating them may be provided This detection may be done for each model within the Natural Language Understanding (NLU) component. For example, the language model, as well as any sentiment or other metadata models used by the NLU, can introduce understanding bias. For each model deployed, training data is automatically analyzed for bias and corrections suggested.

Abstract: Conversation user interfaces that are configured for virtual assistant interaction may include tasks to be completed that may have repetitious entry of the same or similar information. User preferences may be learned by the system and may be confirmed by the user prior to the learned preference being implemented. Learned preferences may be identified in near real-time on large collections of data for a large population of users. Further, the learned preferences may be based at least in part on previous conversations and actions between the system and the user as well as user-defined occurrence thresholds.

Abstract: As described herein, a system for expanding contractions in electronically stored text includes expanding contractions having only on expanded form. For remaining contractions, a grammar check is performed for all possible expanded forms to determine if an expanded form can be selected based on context and grammar rules. If an expanded form is not evident from the first two steps, all possible expanded forms of the remaining contractions are converted to a vector representation along with the original text. A Word Movers Distance (WMD) for each possible expansion is calculated using the vectors for each possible expansion and the original text. An expanded form is chosen without human intervention based on the grammar score alone or the WMD and the grammar score.

Abstract: Hybrid natural language understanding (NLU) systems and methods are provided that capitalize on the strengths of the rule-based models and the statistical models, lowering the cost of development and increasing the speed of construction, without sacrificing control and accuracy. Two models are used for intent recognition, one statistical and one rule-based. Both models define the same set of intents, but the rule-based model is devoid of any grammars or patterns initially. Each model may or may not be hierarchical in that it may be composed of a set of specialized models that are in a tree form or it may be just a singular model.

Abstract: A method and computer product encoding the method is available for preparing a domain or subdomain specific glossary. The method included using probabilities, word context, common terminology and different terminology to identify domain and subdomain specific language and a related glossary updated according to the method.