Abstract: Systems and methods are provided for assessing morality of a user. A request comprising an input data is received over a communication network to assess the morality corresponding to the input data. Upon receiving the request, a first vector is generated through deployment of a predefined language model based on the input data. Then a set of common-sense characteristics are extracted from the input data by generating a corresponding second vector for each of the set of common-sense characteristics from the input data by deploying a common-sense model. Upon generation of the first vector and the second vectors, morality value is determined for the input data based on the first vector and the second vectors corresponding to the set of common-sense characteristics, the morality value indicates whether a context of the input data is morally correct.

Abstract: Present disclosure generally relates to machine translation systems, and particularly to method and system for cross-lingual adaptation using disentangled syntax and shared conceptual latent space for low-resource natural languages. Method includes converting multi-lingual sentences received from user, to linearized constituency parse tree and mask leaf nodes in linearized constituency parse tree to separate semantic information in multi-lingual sentences. Method includes passing linearized constituency parse tree with masked leaf nodes, to syntactic encoder for disentangling syntactic information in multi-lingual sentences. Method includes determining, from syntactic information, if multi-lingual sentences include new language to be learned which includes new script relatively to pre-existing language in language model and unique script with similarities in sentence structure corresponding to pre-existing language.

Abstract: The present disclosure relates to a system for infusing knowledge graphs and language models (LM) for natural language sentence pair tasks, the system include a processor operatively coupled to an inference engine, the inference engine configured to receive a sentence indicative of a premise and a sentence indicative of a hypothesis, extract LM embeddings for the corresponding sentence, generate a common-sense knowledge graph for corresponding sentence and nodes are derived from the common-sense knowledge graph, assign node importance scores for each of the derived nodes, compute node parameters for the derived nodes, apply an aggregation function to generate pooled values, concatenate the LM embeddings, graph embeddings and pooled values to generate concatenated data and classify the concatenation data to indicate a relationship between the natural language inference pair.

Abstract: Systems and methods are provided for assessing morality of a user. A request comprising an input data is received over a communication network to assess the morality corresponding to the input data. Upon receiving the request, a first vector is generated through deployment of a predefined language model based on the input data. Then a set of common-sense characteristics are extracted from the input data by generating a corresponding second vector for each of the set of common-sense characteristics from the input data by deploying a common-sense model. Upon generation of the first vector and the second vectors, morality value is determined for the input data based on the first vector and the second vectors corresponding to the set of common-sense characteristics, the morality value indicates whether a context of the input data is morally correct.

Abstract: The present disclosure pertains to a system (102), and a method (400) for identifying entities and semantic relation between one or more sentences. The system (102) can include a voice to text converter (106), a processor (202), and an output device (108). The processer (202) can be configured to receive one or more sentences from the voice to text converter (106), and extract a pre-defined category pertaining to one or more entities, where the processor is configured to calculate a semantic relation based on the masked out each of one or more entities and facilitates computing semantic similarity and pre-defined category-wise each of the one or more entities difference between the one or more sentences, where the processor (202) can be configured to calculate semantic relation in multiple languages. The processor (202) can be configured to transmit the calculated semantic relation to the output device (108) enables displaying the difference between the one or more sentences.