公开(公告)号：US20210224474A1

公开(公告)日：2021-07-22

申请号：US16746009

申请日：2020-01-17

Applicant: Apple Inc.

Inventor： Jerome R. BELLEGARDA ,  Bishal BARMAN ,  Douglas DAVIDSON

IPC: G06F40/253 ,  G06F40/30 ,  G06N3/04

Abstract: Systems and processes for operating an intelligent automated assistant are provided. In one example process a set of words including a grammatical error is received. The process can generate, using a neural network based on the set of words including the grammatical error and a reference set of words, a transformed set of words and further determine, based on the set of words including the grammatical error and the reference set of words, a reconstructed reference set of words. The process can also determine, based on a comparison of the transformed set of words and the reconstructed reference set of words, whether the transformed set of words is grammatically correct and provide an indication of whether the transformed set of words is grammatically correct to the neural network.

公开(公告)号：US20150347382A1

公开(公告)日：2015-12-03

申请号：US14720655

申请日：2015-05-22

Applicant: Apple Inc.

Inventor： Jannes DOLFING ,  Brent RAMERTH ,  Douglas DAVIDSON ,  Jerome BELLEGARDA ,  Jennifer MOORE ,  Andreas EMINIDIS ,  Joshua SHAFFER

IPC: G06F17/27

CPC classification number: G06F17/276 ,  G06F17/277

Abstract: 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.

Abstract translation: 提供了预测文本输入的系统和过程。 在一个示例过程中，可以接收文本输入。 文本输入可以与输入上下文相关联。 可以使用语言模型来确定相对于语料库子集的出现频率。 子集可以与上下文相关联。 可以基于输入上下文和上下文之间的相似度来确定加权因子。 给出文本输入的预测文本的加权概率可以基于m-gram的出现频率和加权因子来确定。 文字可以包括文本输入中的至少一个单词和预测文本中的至少一个单词。