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Transfer learning improves question classification in new domains by leveraging deep learning models like ALBERT. This approach addresses data scarcity, enhancing accuracy for question answering systems.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Question classification is crucial for question answering (QA) systems, impacting answer accuracy and type restriction.
  • Traditional methods require extensive manually labeled data, which is often unavailable in new domains.
  • Data scarcity poses a significant challenge for developing robust QA systems in emerging fields.

Purpose of the Study:

  • To investigate the effectiveness of various deep transfer learning methods for cross-domain question classification.
  • To address the limitations of manual data labeling in new or specialized domains.
  • To enhance the performance of question answering systems through improved question classification.

Main Methods:

  • Utilized the ALBERT fine-tuning model as a foundation for deep transfer learning.
  • Extracted source domain labels, question text, and target domain predicted labels as input.
  • Incorporated semantic information of category labels and employed WordNet for question expansion.
  • Compared the performance of different deep transfer learning techniques for cross-domain tasks.

Main Results:

  • Deep transfer learning methods, particularly with ALBERT, significantly improve cross-domain question classification.
  • Semantic information extraction and WordNet-based question expansion further boosted classification accuracy in target domains.
  • The proposed approach effectively mitigates the challenges posed by limited labeled data in new domains.

Conclusions:

  • Deep transfer learning offers a viable solution for overcoming data scarcity in cross-domain question classification.
  • Combining ALBERT fine-tuning with semantic information and WordNet expansion enhances QA system performance.
  • This research provides a practical framework for building accurate question classification models in data-limited environments.