Abstract: Methods and systems for training a language model include retrieving a knowledge sentence, related to an input sentence, from a knowledge base. The input sentence, the knowledge sentence, and a prompt are encoded into an intermediate representation. The intermediate representation is decoded to generate a named entity from the input sentence that is of a type specified by the prompt. A language model is fine-tuned based on the named entity.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: Methods and systems for event detection include training a joint neural network model with respective neural networks for audio data and video data relating to a same scene. The joint neural network model is configured to output a belief value, a disbelief value, and an uncertainty value. It is determined that an event has occurred based on the belief value, the disbelief value, and the uncertainty value.

Abstract: A computer-implemented method for personalizing heterogeneous clients is provided. The method includes initializing a federated modular network including a plurality of clients communicating with a server, maintaining, within the server, a heterogenous module pool having sub-blocks and a routing hypernetwork, partitioning the plurality of clients by modeling a joint distribution of each client into clusters, enabling each client to make a decision in each update to assemble a personalized model by selecting a combination of sub-blocks from the heterogenous module pool, and generating, by the routing hypernetwork, the decision for each client.

Abstract: Systems and methods are provided for automated computer code editing. The method includes training a code-editing neural network model using a corpus of code editing data samples, including the pre-editing samples and post-editing samples, and parsing the pre-editing samples and post-editing samples into an Abstract Syntax Tree (AST). The method further includes using a grammar specification to transform the AST tree into a unified Abstract Syntax Description Language (ASDL) graph for different programming languages, and using a gated graph neural network (GGNN) to compute a vector representation for each node in the unified Abstract Syntax Description Language (ASDL) graph. The method further includes selecting and aggregating support samples based on a query code with a multi-extent ensemble method, and altering the query code iteratively using the pattern learned from the pre- and post-editing samples.

Abstract: A method for employing a temporal context-aware question routing model (TCQR) in multiple granularities of temporal dynamics in community-based question answering (CQA) systems is presented. The method includes encoding answerers into temporal context-aware representations based on semantic and temporal information of questions, measuring answerers expertise in one or more of the questions as a coherence between the temporal context-aware representations of the answerers and encodings of the questions, modeling the temporal dynamics of answering behaviors of the answerers in different levels of time granularities by using multi-shift and multi-resolution extensions, and outputting answers of select answerers to a visualization device.

Abstract: Methods and systems of training and using a neural network model include training a time series embedding model and a text embedding model with unsupervised clustering to translate time series and text, respectively, to a shared latent space. The time series embedding model and the text embedding model are further trained using semi-supervised clustering that samples training data pairs of time series information and associated text for annotation.

Abstract: A method for implementing a self-attentive encoder-decoder transformer framework for anomaly detection in event sequences is presented. The method includes feeding event content information into a content-awareness layer to generate event representations, inputting, into an encoder, event sequences of two hierarchies to capture long-term and short-term patterns and to generate feature maps, adding, in the decoder, a special sequence token at a beginning of an input sequence under detection, during a training stage, applying a one-class objective to bound the decoded special sequence token with a reconstruction loss for sequence forecasting using the generated feature maps from the encoder, and during a testing stage, labeling any event representation whose decoded special sequence token lies outside a hypersphere as an anomaly.

Abstract: A computer-implemented method executed by at least one processor for software bug localization is presented. The method includes constructing a bug localization graph to capture relationships between bug tickets and relevant source code files from historical change-sets and an underlying source code repository, leveraging natural processing language tools to evaluate semantic similarity between a new bug ticket and a historical ticket, in response to the evaluated semantic similarity, for the new bug ticket, adding links between the new bug ticket a set of similar historical tickets, incorporating the new bug ticket in the bug localization graph, and developing a mathematical graph expression to determine a closeness relationship between the relevant source code files and the new bug ticket.

Abstract: A computer-implemented method for multi-model representation learning is provided. The method includes encoding, by a trained time series (TS) encoder, an input TS segment into a TS-shared latent representation and a TS-private latent representation. The method further includes generating, by a trained text generator, a natural language text that explains the input TS segment, responsive to the TS-shared latent representation, the TS-private latent representation, and a text-private latent representation.

Abstract: A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.

Abstract: Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.

Abstract: Systems and methods are provided for automated computer code editing. The method includes training a code-editing neural network model using a corpus of code editing data samples, including the pre-editing samples and post-editing samples, and parsing the pre-editing samples and post-editing samples into an Abstract Syntax Tree (AST). The method further includes using a grammar specification to transform the AST tree into a unified Abstract Syntax Description Language (ASDL) graph for different programming languages, and using a gated graph neural network (GGNN) to compute a vector representation for each node in the unified Abstract Syntax Description Language (ASDL) graph. The method further includes selecting and aggregating support samples based on a query code with a multi-extent ensemble method, and altering the query code iteratively using the pattern learned from the pre- and post-editing samples.

Abstract: A computer-implemented method is provided for keyphrase generation. The method includes pretraining, by a processor device, a policy neural network on training documents using a sequence-to-sequence model. The training documents are each associated with a list of keyphrases included therein. The method further includes training, by the processor device, the policy neural network using reinforcement learning with a summarization reward on present annotated keyphrases in an input training document and absent annotated keyphrase from the input training document that semantically describe a concept of the input training document. The method also includes predicting, by the processor device, new keyphrases using the trained policy neural network.

Abstract: A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.

Abstract: A method for detecting business intent from a business intent corpus by employing an Intent Detection via Multi-hop Unified Syntactic Graph (IDMG) is presented. The method includes parsing each text sample representing a business need description to extract syntactic information including at least tokens and words, tokenizing the words of the syntactic information to generate sub-words for each of the words by employing a multi-lingual pre-trained language model, aligning the generated sub-words to the tokens of the syntactic information to match ground-truth intent actions and objects to the tokenized sub-words, generating a unified syntactic graph, encoding, via a multi-hop unified syntactic graph encoder, the unified syntactic graph to generate an output, and predicting an intent action and object from the output.

Abstract: Systems and methods are provided for detail matching. The method includes training a feature classifier to identify technical features, and training a neural network model for a trained importance calculator to calculate an importance value for each identified technical feature. The method further includes receiving a specification sheet including a plurality of technical features, and receiving a plurality of descriptive sheets each including a plurality of technical features. The method further includes identifying the technical features in the specification sheet and the plurality of descriptive sheets using the trained feature classifier, and calculating an importance for each identified technical feature using the trained feature importance calculator.

Abstract: A method for employing a knowledge-driven pre-training framework for learning product representation is presented. The method includes learning contextual semantics of a product domain by a language acquisition stage including a context encoder and two language acquisition tasks, obtaining multi-faceted product knowledge by a knowledge acquisition stage including a knowledge encoder, skeleton attention layers, and three heterogeneous embedding guided knowledge acquisition tasks, generating local product representations defined as knowledge copies (KC) each capturing one facet of the multi-faceted product knowledge, and generating final product representation during a fine-tuning stage by combining all the KCs through a gating network.