Neural Network Transfer Learning for Natural Language Processing

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Source: Zhuangzhi (ID: Quan_Zhuanzhi)

[New Intelligence Yuan Guide] In reality, natural language processing faces various types of tasks across multiple domains and languages, making it impractical to label data for each task individually. Transfer learning allows for the transfer of learned knowledge to related scenarios. This article introduces Dr. Sebastian Ruder’s defense PPT on neural network transfer learning for natural language processing.

Active technical blogger in the NLP field, Sebastian Ruder recently graduated with a PhD and will soon start his career as an AI researcher at DeepMind.

Dr. Sebastian Ruder’s defense PPT titled “Neural Transfer Learning for Natural Language Processing” discusses the motivations, current research status, shortcomings, and his own work regarding transfer learning in natural language.

In the PPT, Dr. Sebastian Ruder elaborated on the motivations for using transfer learning:

• State-of-the-art supervised learning algorithms are relatively fragile:

• Prone to adversarial examples

• Prone to noisy data

• Prone to interpretation issues

• In reality, natural language processing faces various types of tasks across multiple domains and languages, making it impractical to label data for each task individually, while transfer learning can transfer learned knowledge to related scenarios.

• Many foundational cutting-edge NLP technologies can be considered as transfer learning:

• Latent Semantic Analysis

• Brown Clusters

• Pretrained Word Embeddings

Existing transfer learning methods often have the following limitations:

• Over-constrained: predefined similarity metrics, hard parameter sharing

• Custom setting: evaluation on one task, task-level sharing strategies

• Weak baseline: lack of comparison with traditional methods

• Fragile: poor performance out of domain, relying on language and task similarity

• Inefficient: requires more parameters, time, and samples

Therefore, the author believes that research on transfer learning needs to address the following issues:

• Overcoming the gap between source and target

• Inducing inductive bias

• Combining traditional and existing methods

• Cross-layer transfer in NLP tasks

• Generalization settings

The author has conducted work in four areas regarding transfer learning:

• Domain Adaptation

• Cross-lingual Learning

• Multi-task Learning

• Sequential Transfer Learning

For specific content, please refer to Dr. Sebastian Ruder’s complete defense PPT.

PPT download link:

• https://drive.google.com/file/d/1Jhzd8gWK7M_76t1WfNBcB5gzPIAYZAS1/view

This article is reproduced from Zhuangzhi (ID: Quan_Zhuanzhi), please click to read the original text.

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