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.

Systems and processes for animating an avatar are provided. An example process of animating an avatar includes at an electronic device having one or more processors and memory, receiving text, determining an emotional state, and generating, using a neural network, a speech data set representing the received text and a set of parameters representing one or more movements of an avatar based on the received text and the determined emotional state.

Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

A computer is caused to execute: acquiring the log; generating a natural sentence based on the acquired log; and generating story content to be appreciated by a user by arranging the generated natural sentence and one or a plurality of game-related contents that are related to the log.

A computer is caused to execute: acquiring the log; generating a natural sentence based on the acquired log; and generating story content to be appreciated by a user by arranging the generated natural sentence and one or a plurality of game-related contents that are related to the log.

Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

A computer-implemented method, computerized apparatus and computer program product for claim generation, the method comprising: selecting at least one subject according to a given topic; selecting at least one verb from a first data source; selecting at least one object from a second data source; generating one or more candidate claim sentences, each of which composed of a subject selected from the at least one subject, a verb selected from the at least one verb and an object selected from the at least on object; and determining validity of the candidate claim sentences using a machine learning process.

The disclosed technology provides computer-to-wireless-voice integration methods and systems. In some implementations, the methods and systems deliver real-time voice instructions to users of required time-sensitive actions and ensure that such directives are received and a recipient effectively acts on the directives. The systems and methods include receiving a notification of an event from a terminal in a wireless active voice engine (WAVE) system, determining an active voice directive corresponding to the event with a WAVE module, converting the active voice directive into a voice event via a directive converter, and notifying a targeted recipient of the active voice directive corresponding to the event with a communications module. In some implementations, the systems and methods include sending a confirmation event via the receiver to the communications module that the active voice directive was received by the targeted recipient and communicating the active voice directive has been completed.