Summary of Don’t Just Pay Attention, Plant It: Transfer L2r Models to Fine-tune Attention in Extreme Multi-label Text Classification, by Debjyoti Saharoy et al.
Don’t Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification
by Debjyoti Saharoy, Javed A. Aslam, Virgil Pavlu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of fine-tuning Extreme Multi-Label Text Classification (XMTC) models for optimal attention weights. They introduce PLANT, a transfer learning strategy that leverages a pretrained Learning-to-Rank model as a planted attention layer to focus on key tokens in input text. The proposed method surpasses existing state-of-the-art methods across multiple datasets and particularly excels in few-shot scenarios. Key innovations include leveraging mutual-information gain to enhance attention, introducing an inattention mechanism, and implementing a stateful-decoder to maintain context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve the performance of machine learning models that can understand many labels at once. The researchers developed a new way to fine-tune these models, called PLANT. This method works better than others on several important datasets and is especially good when we have very little data to train with. The team made some key changes to make this work well, including using mutual information to help the model focus on important parts of the text. |
Keywords
» Artificial intelligence » Attention » Decoder » Few shot » Fine tuning » Machine learning » Text classification » Transfer learning
