Systems and processes for multilingual word prediction are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, identifying context information of the electronic device and generating, with the one or more processors, a plurality of candidate words based on the context information, wherein a first candidate word of the plurality of candidate words corresponds to a first language of a plurality of languages and a second candidate word of the plurality of candidate words corresponds to a second language of the plurality of languages different than the first language.

An information terminal with low power consumption is provided. The information terminal includes a liquid crystal element, a light-emitting element, a first transistor, and a touch sensor. The touch sensor includes a photodiode, a second transistor, and a third transistor. The first transistor has a function of controlling a current flowing through the light-emitting element. The photodiode is electrically connected to a gate of the third transistor through the second transistor. A gate of the first transistor is electrically connected to the gate of the third transistor through at least one transistor.

A method and method for natural language generation employ a natural language generation model which has been trained to assign an utterance label to a new text sequence, based on features extracted from the text sequence, such as parts-of-speech. The model assigns an utterance label to the new text sequence, based on the extracted features. The utterance label is used to guide the generation of a natural language utterance, such as a question, from the new text sequence. The system and method find application in dialog systems for generating utterances, to be sent to a user, from brief descriptions of problems or solutions in a knowledge base.

A system and method for determining whether a textual work submitted for publishing is machine generated or non-machine generated by identifying and quantifying various aspects of the textual work and comparing those aspects to known works. For example, the system and method may identify aspects of a textual work, including, a relationship between the sentences within the textual work, a writing style of the author of the textual work, a grammatical structure of the sentences within the textual work, a quality of the textual work, and other aspects of the textual work. Upon determining that the textual work is machine generated the textual work may be rejected for publishing.

A system, computer program product, and process are provided for an automated escalation agent. A receiver receives a request for language interpretation from a first language to a second language. Further, a database stores data associated with a plurality of language interpreters associated with a computer implemented language interpretation platform. A processor searches the database and determines that no online language interpreter is available to perform language interpretation at a time of the request and that escalates the request to determine if an offline language interpreter is available to initiate the language interpretation within a predetermined time period measured from the time of the request. An automated escalation agent module searches the database according to notification criteria, and sends at least one notification including the request to a plurality of offline language interpreters that meet the notification criteria.

A mobile terminal and controlling method thereof are provided. The mobile terminal includes a camera, a display unit configured to display a preview image obtained by the camera, and a controller configured to detect a first command for entering a translation mode, output a GUI window for selecting at least one partial region of a text included in the preview image to the display unit in response to the detected first command, control the display unit to display a translation result of a text corresponding the GUI window on the preview image.

Described herein are systems and methods for providing a natural language generator in a spoken dialogue system that considers both lexicalized and delexicalized dialogue act slot-value pairs when translating one or more dialogue act slot-value pairs into a natural language output. Each slot and value associated with the slot in a dialogue act are represented as (dialogue act+slot, value), where the first term (dialogue act+slot) is delexicalized and the second term (value) is lexicalized. Each dialogue act slot-value representation is processed to produce to produce at least one delexicalized sentence as an output. A lexicalized sentence is produced by replacing each delexicalized slot with the value associated with the delexicalized slot.

A computer system gleans data from patient records and clinical trial descriptions using NLP techniques. NLP annotation data is used to generate clinical trial feature vectors and patient feature vectors. Clinical trial feature vectors and patient feature vectors are compared to match appropriate patient candidates with clinical trial openings.

Devices and methods for determining the content of a first segment of text in a first language, using a second segment of text in a second language. The second segment of text is a translation of the first segment of text.

Usage data can be received by a computer system from a sensor-enabled appliance. A new appliance profile can be accessed from an external data source; the new appliance profile can contain price and energy consumption data about the new appliance. The system can also identify relational data, which can be collected from a set of sensor-enabled appliances. The relational data can be used to determine usage trends for the appliances. The usage data, new appliance profile, and relational data can then be used to determine that current usage costs exceed potential usage costs, and this determination can be transmitted to a user device.