Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

Implementations of the subject matter described herein provide a solution for generating a text from the structured data. In this solution, the structured data is converted into its representation, where the structured data comprises a plurality of cells, and the representation of the structured data comprises plurality of representations of the plurality of cells. A natural language sentence associated with the structured data may be determined based on the representation of the structured data, thereby implementing the function of converting the structured data into a text.

A method includes obtaining first training data having multiple first linguistic samples. The method also includes generating second training data using the first training data and multiple symmetries. The symmetries identify how to modify the first linguistic samples while maintaining structural invariants within the first linguistic samples, and the second training data has multiple second linguistic samples. The method further includes training a machine learning model using at least the second training data. At least some of the second linguistic samples in the second training data are selected during the training based on a likelihood of being misclassified by the machine learning model.

A system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide, via the communications module, a first encryption key of an encryption key pair to a client device; receive, via the communications module and from a conversation agent server, a fulfillment request based on a natural language input transmitted from the client device to the conversation agent server; determine that the fulfillment request includes a request for personal data; obtain the requested personal data; encrypt the personal data with a second encryption key of the encryption key pair; and provide, via the communications module and to the conversation agent server, the encrypted personal data for transmission to the client device.

A system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide, via the communications module, a first encryption key of an encryption key pair to a client device; receive, via the communications module and from a conversation agent server, a fulfillment request based on a natural language input transmitted from the client device to the conversation agent server; determine that the fulfillment request includes a request for personal data; obtain the requested personal data; encrypt the personal data with a second encryption key of the encryption key pair; and provide, via the communications module and to the conversation agent server, the encrypted personal data for transmission to the client device.

A method includes receiving a voice request to generate synthesized voiceover speech for a target advertisement having one or more advertising campaign attributes. The method also includes generating, based on the one or more advertising campaign attributes, a voiceover script that includes a sequence of text for the synthesized voiceover speech. The method also includes generating, using a text-to-speech (TTS) system, the synthesized voiceover speech. The TTS system is configured to receive, as input, the sequence of text for the voiceover script and generate, as output, the synthesized voiceover speech. Here, the synthesized voiceover speech has speech characteristics specified by a target TTS vertical. The method also includes overlaying the synthesized voiceover speech on the target advertisement.

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.

Embodiments provide methods and systems for generating domain-specific text summary. Method performed by processor includes receiving request to generate text summary of textual content from user device of user and applying pre-trained language generation model over textual content for encoding textual content into word embedding vectors. Method includes predicting current word of the text summary, by iteratively performing: generating first probability distribution of first set of words using first decoder based on word embedding vectors, generating second probability distribution of second set of words using second decoder based on word embedding vectors, and ensembling first and second probability distributions using configurable weight parameter for determining current word. First probability distribution indicates selection probability of each word being selected as current word. Method includes providing custom reward score as feedback to second decoder based on custom reward model and modifying second probability distribution of words for text summary based on feedback.