Apple Inc.

Jannes G Dolfing

Jerome Rene Bellegarda

Ryan S Dixon

Karan Misra

Karl Martin Groethe

Ueli Meier

Mei-Qun Xia

Xin Ru Wang

Joshua H Shaffer

Alexandre F Carlhian

Brent D Ramerth

Douglas R Davidson

Mathieu Jean Martel

Jennifer Lauren Moore

Sabrine Rekik

Antoine Vinel

Markus Berget

Hafid Arras

Andreas Eminidis

Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition for multi-character handwriting input. In particular, real-time, stroke-order and stroke-direction independent handwriting recognition is provided for multi-character, or sentence level Chinese handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

Managing real-time handwriting recognition

Systems and processes for unified language modeling are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a character of a sequence of characters and determining a current character context based on the received character of the sequence of characters and a previous character context. The method further includes determining a current word representation based on the current character context and determining a current word context based on the current word representation and a previous word context. The method further includes determining a next word representation based on the current word context and providing the next word representation.

Unified language modeling framework for word prediction, auto-completion and auto-correction

The present disclosure generally relates to systems and processes for morpheme-based word prediction. An example method includes receiving a current word; determining a context of the current word based on the current word and a context of a previous word; determining, using a morpheme-based language model, a likelihood of a prefix based on the context of the current word; determining, using the morpheme-based language model, a likelihood of a stem based on the context of the current word; determining, using the morpheme-based language model, a likelihood of a suffix based on the context of the current word; determining a next word based on the likelihood of the prefix, the likelihood of the stem, and the likelihood of the suffix; and providing an output including the next word.

Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling

Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, at a user device with one or more processors and memory, receiving unstructured natural language information from at least one user. The method also includes, in response to receiving the unstructured natural language information, determining whether event information is present in the unstructured natural language information. The method further includes, in accordance with a determination that event information is present within the unstructured natural language information, determining whether an agreement on an event is present in the unstructured natural language information. The method further includes, in accordance with a determination that an agreement on an event is present, determining an event type of the event and providing an event description based on the event type.

Data driven natural language event detection and classification

Systems and processes for language identification using recurrent neural networks are provided. An example method includes, at an electronic device, receiving a first typed character of a character sequence and determining a character context of the first typed character based on the first typed character and a second typed character of the character sequence. The method further includes determining a confidence level that the character sequence is associated with a language of a plurality of languages based on the character context of the first typed character, and determining whether the confidence level exceeds a threshold, in accordance with a determination that the confidence level exceeds the threshold, providing the language as a candidate language, and in accordance with a determination that the confidence level does not exceed the threshold, forgoing providing the language as a candidate language.

Language identification using recurrent neural networks

Systems and processes for efficient word encoding are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input including a word sequence, and providing a representation of a current word of the word sequence. The representation of the current word may be indicative of a class of a plurality of classes and a word associated with the class. The method further includes determining a current word context based on the representation of the current word and a previous word context, and providing a representation of a next word of the word sequence. The representation of the next word of the word sequence may be based on the current word context. The method further includes displaying, proximate to the user input, the next word of the word sequence.

Efficient word encoding for recurrent neural network language models

The present disclosure generally relates to integrated text conversion and prediction. In an example process, a current character input of a first writing system is received. A first current character context in the first writing system is determined based on the current character input and a first previous character context in the first writing system. A second current character context in a second writing system is determined based on the first current character context, a second previous character context in the second writing system, and a character representation in the second writing system. A current word context in the second writing system is determined based on the second current character context, a previous word context in the second writing system, and a word representation in the second writing system. Based on the current word context, a probability distribution over a word inventory in the second writing system is determined.

Unified framework for text conversion and prediction

Systems and processes for predictive text input are provided. In one example process, a text input can be received. The text input can be associated with an input context. A frequency of occurrence of an m-gram with respect to a subset of a corpus can be determined using a language model. The subset can be associated with a context. A weighting factor can be determined based on a degree of similarity between the input context and the context. A weighted probability of a predicted text given the text input can be determined based on the frequency of occurrence of the m-gram and the weighting factor. The m-gram can include at least one word in the text input and at least one word in the predicted text.

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Daly City, CA, United States of America

Jannes G Dolfing