From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

A system and method for translating audio, and video when desired. The translations include synthetic media and data generated using AI systems. Through unique processors and generators executing a unique sequence of steps, the system and method produces more accurate translations that can account for various speech characteristics (e.g., emotion, pacing, idioms, sarcasm, jokes, tone, phonemes, etc.). These speech characteristics are identified in the input media and synthetically incorporated into the translated outputs to mirror the characteristics in the input media. Some embodiments further include systems and methods that manipulate the input video such that the speakers' faces and/or lips appear as if they are natively speaking the generated audio.

A system and method for translating audio, and video when desired. The translations include synthetic media and data generated using AI systems. Through unique processors and generators executing a unique sequence of steps, the system and method produces more accurate translations that can account for various speech characteristics (e.g., emotion, pacing, idioms, sarcasm, jokes, tone, phonemes, etc.). These speech characteristics are identified in the input media and synthetically incorporated into the translated outputs to mirror the characteristics in the input media. Some embodiments further include systems and methods that manipulate the input video such that the speakers' faces and/or lips appear as if they are natively speaking the generated audio.

Methods and apparatus directed to utilizing an automated messaging system to initiate and/or adapt a dialog with at least one user, where the dialog occurs via user interface input and output devices of at least one computing device of the user. In some of those implementations, the automated messaging system identifies at least one task associated with the user and initiates the dialog with the user based on identifying the task. The automated messaging system may initiate the dialog to provide the user with additional information related to the task and/or to determine, based on user input provided during the dialog, values for one or more parameters of the task. In some implementations, the automated messaging system may further initiate performance of the task utilizing parameters determined during the dialog.

Methods and apparatus directed to utilizing an automated messaging system to initiate and/or adapt a dialog with at least one user, where the dialog occurs via user interface input and output devices of at least one computing device of the user. In some of those implementations, the automated messaging system identifies at least one task associated with the user and initiates the dialog with the user based on identifying the task. The automated messaging system may initiate the dialog to provide the user with additional information related to the task and/or to determine, based on user input provided during the dialog, values for one or more parameters of the task. In some implementations, the automated messaging system may further initiate performance of the task utilizing parameters determined during the dialog.

A system and method for translating audio, and video when desired. The translations include synthetic media and data generated using AI systems. Through unique processors and generators executing a unique sequence of steps, the system and method produces more accurate translations that can account for various speech characteristics (e.g., emotion, pacing, idioms, sarcasm, jokes, tone, phonemes, etc.). These speech characteristics are identified in the input media and synthetically incorporated into the translated outputs to mirror the characteristics in the input media. Some embodiments further include systems and methods that manipulate the input video such that the speakers’ faces and/or lips appear as if they are natively speaking the generated audio.

A system and method for translating audio, and video when desired. The translations include synthetic media and data generated using AI systems. Through unique processors and generators executing a unique sequence of steps, the system and method produces more accurate translations that can account for various speech characteristics (e.g., emotion, pacing, idioms, sarcasm, jokes, tone, phonemes, etc.). These speech characteristics are identified in the input media and synthetically incorporated into the translated outputs to mirror the characteristics in the input media. Some embodiments further include systems and methods that manipulate the input video such that the speakers’ faces and/or lips appear as if they are natively speaking the generated audio.

Methods and systems for training a neural language model. Clean sequence pairs are received including clean source and target sequences. For each clean sequence pair, a noisy version is sampled with an adversarial generator to generate a noisy sequence pair. Parameters of the neural language model are optimized on the clean and noisy sequence pairs. Parameters of the adversarial generator are optimized to minimize a modeling loss of the adversarial generator and maximize a neural language loss of the neural language model using backpropagation.

A training method, a text translation method, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of natural language processing and deep learning technologies. A specific implementation solution includes: performing a feature extraction on source sample text data to obtain a sample feature vector sequence; obtaining a target sample feature vector according to the sample feature vector sequence; performing an autoregressive decoding and a non-autoregressive decoding on the sample feature vector sequence, respectively; performing a length prediction on the target sample feature vector; training a predetermined model by using translation sample data, the autoregressive text translation result, the non-autoregressive text translation result, a true length value of the source sample text, the first predicted length value, a true length value of the translation sample text, and the second predicted length value to obtain the text translation model.