Abstract: In an embodiment, the disclosed technologies are capable of receiving, by a digital model, data representing a first text sequence in a first language; using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language; and outputting, by the digital model, the second text sequence in the first language. The modifying may include any one or more of: deleting text from the first text sequence, adding text to the first text sequence, modifying text of the first text sequence, reordering text of the first text sequence, adding a digital markup to the first text sequence. The digital model may have been fine-tuned, after having been machine-learned, using a subset of values of model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer.

Abstract: In an embodiment, the disclosed technologies are capable of receiving, by a digital model, data representing a first text sequence in a first language; using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language; and outputting, by the digital model, the second text sequence in the first language. The modifying may include any one or more of: deleting text from the first text sequence, adding text to the first text sequence, modifying text of the first text sequence, reordering text of the first text sequence, adding a digital markup to the first text sequence. The digital model may have been fine-tuned, after having been machine-learned, using a subset of values of model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for aiding in the audio-visual composition of electronic communications.

Abstract: A method of electronic communication assistance is provided. The method includes receiving, via an artificial intelligence assistant computing facility, an electronic communication from a first user intended to be received by a second user; and determining, via the artificial intelligence assistant computing facility, a capacity of the second user to receive the electronic communication. The method further includes determining, via the artificial intelligence assistant computing facility and based at least in part on the capacity of the second user, a time to send the electronic communication; and transmitting, via the artificial intelligence assistant computing facility, the time to the first user.

Abstract: A method of electronic communication assistance is provided. The method includes receiving, via an artificial intelligence assistant computing facility, an electronic communication from a first user intended to be received by a second user; and determining, via the artificial intelligence assistant computing facility, a capacity of the second user to receive the electronic communication. The method further includes determining, via the artificial intelligence assistant computing facility and based at least in part on the capacity of the second user, a time to send the electronic communication; and transmitting, via the artificial intelligence assistant computing facility, the time to the first user.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

Abstract: In embodiments of the present invention improved capabilities are described for an artificial intelligence communication assistant improving the effectiveness of communications with user reaction data.

Abstract: In embodiments of the present invention improved capabilities are described for an artificial intelligence communication assistant augmenting a communication after it has been transmitted.

Abstract: Systems and methods are provided for the detection of sentiment in writing. A plurality of texts is received from a larger collection of writing samples with a computer system. A set of seed words from the plurality of texts are labeled as being of positive sentiment or of negative sentiment with the computer system. The set of seed words is expanded in size with the computer system to provide an expanded set of seed words. Intensity values are assigned to words of the expanded set of seed words. Each of the words of the expanded set of seed words is assigned three intensity values: a value corresponding to the strength of the word's association with a positive polarity class, a value corresponding to the strength of the word's association with a negative polarity class, and a value corresponding to the strength of the word's association with a neutral polarity class.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for aiding in the composition of electronic communications utilizing user communication profiles.

Abstract: An electronic message composition support system, method and architecture including machine learning and natural language processing techniques for extending message composition capability and support and to provide feedback to a user regarding an error, condition, etc., detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for the editing of electronic communications utilizing user communication profiles.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for aiding in the audio-visual composition of electronic communications.

Abstract: In embodiments of the present invention improved capabilities are described for a sender-receiver interface for artificial intelligence communication assistance for augmenting a communication after it has been transmitted.

Abstract: In an embodiment, the disclosed technologies are capable of receiving, by a digital model, data representing a first text sequence in a first language; using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language; and outputting, by the digital model, the second text sequence in the first language. The modifying may include any one or more of: deleting text from the first text sequence, adding text to the first text sequence, modifying text of the first text sequence, reordering text of the first text sequence, adding a digital markup to the first text sequence. The digital model may have been fine-tuned, after having been machine-learned, using a subset of values of model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer.

Abstract: Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for providing advice in drafting an electronic communication to a target recipient utilizing user communication profiles.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

Abstract: In embodiments of the present invention improved capabilities are described for an artificial intelligence communication assistant augmenting a communication after it has been transmitted.

Abstract: In embodiments of the present invention improved capabilities are described for artificial intelligence communication assistance for aiding in the audio-visual composition of electronic communications.