18151186. TEXT DATA PROCESSING METHOD AND APPARATUS simplified abstract (Huawei Technologies Co., Ltd.)

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TEXT DATA PROCESSING METHOD AND APPARATUS

Organization Name

Huawei Technologies Co., Ltd.

Inventor(s)

Tong Cui of Shenzhen (CN)

Jinghui Xiao of Shenzhen (CN)

Liangyou Li of Shenzhen (CN)

TEXT DATA PROCESSING METHOD AND APPARATUS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18151186 titled 'TEXT DATA PROCESSING METHOD AND APPARATUS

Simplified Explanation

The patent application describes a method for processing text data using a noise generation model and a text processing model.

  • The method involves obtaining a target text and processing it using a noise generation model to obtain a noisy text.
  • The noise generation model is trained using training data that includes a correct text corresponding to speech data and a second text obtained through speech recognition using a speech recognition model.
  • A text processing model is then trained using the noisy text as training data to obtain a trained text processing model.

Potential Applications

  • Text data processing for speech recognition systems.
  • Natural language processing tasks such as machine translation, sentiment analysis, and text summarization.

Problems Solved

  • Improving the accuracy of text data processing by incorporating noise generation and training models.
  • Addressing the challenges of processing noisy text data obtained from speech recognition systems.

Benefits

  • Enhanced performance of speech recognition systems by training models using noisy text data.
  • Improved accuracy and efficiency in natural language processing tasks.
  • Potential for more accurate and reliable machine translation, sentiment analysis, and text summarization.


Original Abstract Submitted

This application discloses example text data processing method. One example method includes obtaining a target text. The target text can then be processed based on a noise generation model to obtain a noisy text, where when the noise generation model is trained, training data of the noise generation model at least includes a first text and a second text, the first text is a correct text corresponding to speech data, and the second text is obtained by performing speech recognition on the speech data by using a first speech recognition model. A text processing model can then be trained, by using at least the noisy text as training data, to obtain a trained text processing model.