Grammarly, Inc.

Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Joel Tetreault

Oleksiy Shevchenko

Bradley Jon Hoover

Maksym Lytvyn

Dmytro Lider

Ayan Mandal

Aasish Kumar Pappu

Ellie Pavlick

Yoelle Smadja Maarek

Edo Liberty

Meizhu Liu

Liangliang Cao

Maria Nadejde

Gilad Tsur

Amanda Joy Stent

Yashar Mehdad

Sungjin Lee

Courtney Napoles Cohen

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.

Proficiency and native language-adapted grammatical error correction

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.

Artificial intelligence communication assistance

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.

Artificial intelligence communication assistance in audio-visual composition

Artificial intelligence communication assistance for providing communication advice utilizing communication profiles

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.

Method and system for automatic formality classification

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.

Artificial intelligence communication assistance for improving the effectiveness of communications using reaction data

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

Artificial intelligence communication assistance for augmenting a transmitted communication

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.

Artificial intelligence communication assistance for composition utilizing communication profiles

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.

Electronic message composition support method and apparatus

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.

Artificial intelligence communication assistance for editing utilizing communication profiles

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.

Sender-receiver interface for artificial intelligence communication assistance for augmenting communications

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.

Detecting abusive language using character N-gram features

One or more computing devices, systems, and/or methods for commentary generation are provided. For example, a conversation, occurring through a conversation interface associated with a content item, is monitored to identify a tone of the conversation (e.g., users discussing a news article). If the tone deviates from a target tone (e.g., a negative tone of inflammatory comments, a low participation tone, an off topic tone, etc.), then intervention is automatically and programmatically performed for the conversation. For example, subject matter of the content item, information from external sources (e.g., other articles, social network posts, or website content associated with a topic of the news article), and/or programmatically generated information (e.g., topical statements generated by a neural network) are used to construct a comment. The comment is posted to the conversation interface in order to improve the conversation, such as to increase positive engagement by users.

Growing community of inventors

New York, NY, United States of America

Joel Tetreault