Deep Learning for the Soft Cutoff Problem

Exploring deep learning techniques for solving the soft cutoff problem, this study by Miles Saffran discusses the MATERIAL project, data collection, methods like query embedding and TensorFlow construction, and presents results with training loss trends and performance variances. The conclusion suggests adding more features, using more training data, and incorporating dropout and regularization for improved results.

• Deep learning
• Soft cutoff problem
• Data collection
• TensorFlow
• Performance evaluation

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Uploaded on Sep 17, 2024 | 0 Views

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Presentation Transcript

1. Deep Learning for the Soft Cutoff Problem Miles Saffran

2. Introduction The MATERIAL project The soft cutoff problem Metric of evaluation

3. Materials and Methods Data collection Input features Query embedding Document length Indri document score Construction TensorFlow

4. Results Figure 1. Training loss over epochs

5. Results Figure 2. English loss with different learning rates

6. Results

7. Results

8. Results

9. Results Variance in performance .1 on English to English (optimal .14) .15 on Tagalog to Swahili (optimal .35)

10. Conclusion Add more features Use more training data Include dropout and regularization