Abstract: Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.

Abstract: In some examples, user journey carbon footprint reduction may include generating, for a vehicle associated with a user, a carbon emission quota for user journey carbon footprint reduction. A predicted journey carbon emissions may be generated for the vehicle for a specified journey. Based on collaborative filtering, at least one goal-based and conditions-based recommendation may be generated for the user of the vehicle for the specified journey for the user journey carbon footprint reduction. Based on a user behavior model, a user-interface display may be generated for the specified journey for the user journey carbon footprint reduction. Further, based on the user behavior model, and real-time monitoring of the user and the vehicle, a real-time update of the user-interface display may be generated for the specified journey for the user journey carbon footprint reduction.

Abstract: Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems.

Abstract: Systems and methods for Deep Learning techniques based multi-purpose conversational agents for processing natural language queries. The traditional systems and methods provide for conversational systems for processing natural language queries but do not employ Deep Learning techniques, and thus are unable to process large number of intents. Embodiments of the present disclosure provide for Deep Learning techniques based multi-purpose conversational agents for processing the natural language queries by defining and logically integrating a plurality of components comprising of multi-purpose conversational agents, identifying an appropriate agent to process one or more natural language queries by a High Level Intent Identification technique, predicting a probable user intent, classifying the query, and generate a set of responses by querying or updating one or more knowledge graphs.

Abstract: Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems.

Abstract: Systems and methods for Deep Learning techniques based multi-purpose conversational agents for processing natural language queries. The traditional systems and methods provide for conversational systems for processing natural language queries but do not employ Deep Learning techniques, and thus are unable to process large number of intents. Embodiments of the present disclosure provide for Deep Learning techniques based multi-purpose conversational agents for processing the natural language queries by defining and logically integrating a plurality of components comprising of multi-purpose conversational agents, identifying an appropriate agent to process one or more natural language queries by a High Level Intent Identification technique, predicting a probable user intent, classifying the query, and generate a set of responses by querying or updating one or more knowledge graphs.

Abstract: Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.