Abstract: A transcription of audio speech included in electronic content associated with a meeting is created by an ASR model trained on speech-to-text data. The transcription is post-processed by modifying text included in the transcription, for example, by modifying punctuation, grammar, or formatting introduced by the ASR model and by changing or omitting one or more words that were included in both the audio speech and the transcription. After the transcription is post-processed, output based on the post-processed transcription is generated in the form of a meeting summary and/or template.

Abstract: Described are a method for suppressing overshoot of an output voltage or output current, a charging device, and a medium. The method for suppressing the overshoot of the output voltage or output current includes the following. A loop in an open-loop state in a closed-loop control circuit is determined. A wave-sending control value output by the closed-loop control circuit at a present beat is obtained. The wave-sending control value output at the present beat is assigned to an open-loop output value at the present beat, where the open-loop output value is an output value of the loop in the open-loop state. An open-loop output value of a loop in the open-loop state at a next beat is calculated by using an assigned open-loop output value at the present beat.

Abstract: This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-semantically-conditioned generative model that has been pretrained based at least on a first training data set having unlabeled training examples and semantically conditioned based at least on a second training data set having dialog act-labeled utterances. The method or technique can also include inputting dialog acts into the semantically-conditioned generative model and obtaining synthetic utterances that are output by the semantically-conditioned generative model. The method or technique can also include outputting the synthetic utterances.

Abstract: Systems and techniques are provided for facilitating the automatic discovery and application of rules for refining the training of pretrained models, such as natural language processing models. Weak symbolic rules are automatically generated from the identification and processing of sparse labeled data by the pretrained model(s). Once the weak rules are generated, they are integrated into the model(s) via an attention mechanism to supplement the direct training performed by the sparse labeled data and to thereby boost a supervision signal generated by the sparse labeled data on any newly processed unlabeled data in the intended runtime environment(s) where the models are applied.

Abstract: A language module is joint trained with a knowledge module for natural language understanding by aligning a first knowledge graph with a second knowledge graph. The knowledge module is trained on the aligned knowledge graphs. Then, the knowledge module is integrated with the language module to generate an integrated knowledge-language module.

Abstract: Some disclosed systems are configured to obtain a knowledge module configured to receive one or more knowledge inputs corresponding to one or more different modalities and generate a set of knowledge embeddings to be integrated with a set of multi-modal embeddings generated by a multi-modal main model. The systems receive a knowledge input at the knowledge module, identify a knowledge type associated with the knowledge input, and extract a knowledge unit from the knowledge input. The systems select a representation model that corresponds to the knowledge type and select a grounding type configured to ground the at least one knowledge unit into the representation model. The systems then ground the knowledge unit into the representation model according to the grounding type.

Abstract: A transcription of audio speech included in electronic content associated with a meeting is created by an ASR model trained on speech-to-text data. The transcription is post-processed by modifying text included in the transcription, for example, by modifying punctuation, grammar, or formatting introduced by the ASR model and by changing or omitting one or more words that were included in both the audio speech and the transcription. After the transcription is post-processed, output based on the post-processed transcription is generated in the form of a meeting summary and/or template.

Abstract: A transcription of audio speech included in electronic content associated with a meeting is created by an ASR model trained on speech-to-text data. The transcription is post-processed by modifying text included in the transcription, for example, by modifying punctuation, grammar, or formatting introduced by the ASR model and by changing or omitting one or more words that were included in both the audio speech and the transcription. After the transcription is post-processed, output based on the post-processed transcription is generated in the form of a meeting summary and/or template.

Abstract: This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-adapted generative model that has been tuned using one or more task-specific seed examples. The method or technique can also include inputting dialog acts into the task-adapted generative model and obtaining synthetic utterances that are output by the task-adapted generative model. The method or technique can also include populating a synthetic training corpus with synthetic training examples that include the synthetic utterances. The synthetic training corpus may be suitable for training a natural language understanding model.

Abstract: This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-adapted generative model that has been tuned using one or more task-specific seed examples. The method or technique can also include inputting dialog acts into the task-adapted generative model and obtaining synthetic utterances that are output by the task-adapted generative model. The method or technique can also include populating a synthetic training corpus with synthetic training examples that include the synthetic utterances. The synthetic training corpus may be suitable for training a natural language understanding model.

Abstract: According to one or more embodiments, operations may include gathering a set of machine learning (ML) projects from one or more repositories of ML projects based on a filtering criteria. The operations may also include ensuring executability of ML pipelines in the set of ML projects. In addition, the operations may include identifying irrelevant portions of the ML pipelines in the set of ML projects. Moreover, the operations may include generating quality features for the set of ML projects. In addition, the operations may include generating diversity features for the set of ML projects. Moreover, the operations may include selecting a subset of ML projects from the set of ML projects based on the quality features and the diversity features. In addition, the operations may include storing the subset of ML projects in a corpus of ML projects that may be adapted for use in new ML projects.

Abstract: A language module is joint trained with a knowledge module for natural language understanding by aligning a first knowledge graph with a second knowledge graph. The knowledge module is trained on the aligned knowledge graphs. Then, the knowledge module is integrated with the language module to generate an integrated knowledge-language module.

Abstract: A speech module is joint trained with a knowledge module by transforming a first knowledge graph into an acoustic knowledge graph. The knowledge module is trained on the acoustic knowledge graph. Then, the knowledge module is integrated with the speech module to generate an integrated knowledge-speech module. In some instances, the speech module included in the integrated knowledge-speech module is aligned with a language module to generate an optimized speech model configured to leverage acoustic information and acoustic-based knowledge information, along with language information.

Abstract: A system is provided for generating an optimized speech model by training a knowledge module on a knowledge graph. A language module is trained on unlabeled text data and a speech module is trained on unlabeled acoustic data. The knowledge module is integrated with the language module to perform semantic analysis using knowledge-graph based information. The speech module is then aligned to the language module of the integrated knowledge-language module. The speech module is then configured as an optimized speech model configured to leverage acoustic and language information in natural language processing tasks.

Abstract: This document relates to machine learning. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining a task-adapted generative model that has been tuned using one or more task-specific seed examples. The method or technique can also include inputting dialog acts into the task-adapted generative model and obtaining synthetic utterances that are output by the task-adapted generative model. The method or technique can also include populating a synthetic training corpus with synthetic training examples that include the synthetic utterances. The synthetic training corpus may be suitable for training a natural language understanding model.

Abstract: According to one or more embodiments, operations may include gathering a set of machine learning (ML) projects from one or more repositories of ML projects based on a filtering criteria. The operations may also include ensuring executability of ML pipelines in the set of ML projects. In addition, the operations may include identifying irrelevant portions of the ML pipelines in the set of ML projects. Moreover, the operations may include generating quality features for the set of ML projects. In addition, the operations may include generating diversity features for the set of ML projects. Moreover, the operations may include selecting a subset of ML projects from the set of ML projects based on the quality features and the diversity features. In addition, the operations may include storing the subset of ML projects in a corpus of ML projects that may be adapted for use in new ML projects.

Abstract: A transcription of audio speech included in electronic content associated with a meeting is created by an ASR model trained on speech-to-text data. The transcription is post-processed by modifying text included in the transcription, for example, by modifying punctuation, grammar, or formatting introduced by the ASR model and by changing or omitting one or more words that were included in both the audio speech and the transcription. After the transcription is post-processed, output based on the post-processed transcription is generated in the form of a meeting summary and/or template.

Abstract: Conversational virtual assistance for delivering relevant query solutions is provided. A virtual assistant system comprises various components associated with developing a knowledge database that can be searched for finding documents that fulfill the user's intent. The virtual assistant system further comprises components for receiving a query from a user, extracting entities for understanding the user's intent, and for searching a knowledge database for documents responsive to the query. When additional information is needed for determining more relevant results, a conversation strategy is determined, and a question is formulated for generating a conversation with the user for clarifying the user's intent, confirming a solution, or obtaining additional information. The user is enabled to provide a follow-up response that is related to a previously identified entity. The entity is edited in the query, and responses are refined responsive to the edited query.

Abstract: Systems and methods for machine comprehension are provided. In example embodiments, a machine accesses a context and a question related to the context. The machine determines a low-level meaning of the question and a low-level meaning of the context, the low-level meaning corresponding to words or phrases. The machine determines a high-level meaning of the question and a high-level meaning of the context, the high-level meaning corresponding to sentences or paragraphs. The machine computes, for each position i in the context, a first probability that an answer to the question starts at the position i. The machine computes, for each position j in the context, a second probability that the answer to the question ends at the position j. The machine determines the answer to the question based on the computed first probabilities and the computed second probabilities.

Abstract: Conversational virtual assistance for delivering relevant query solutions is provided. A virtual assistant system comprises various components associated with developing a knowledge database that can be searched for finding documents that fulfill the user's intent. The virtual assistant system further comprises components for receiving a query from a user, extracting entities for understanding the user's intent, and for searching a knowledge database for documents responsive to the query. When additional information is needed for determining more relevant results, a conversation strategy is determined, and a question is formulated for generating a conversation with the user for clarifying the user's intent, confirming a solution, or obtaining additional information. The user is enabled to provide a follow-up response that is related to a previously identified entity. The entity is edited in the query, and responses are refined responsive to the edited query.