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Adaptive Language Modeling With Minimum Discriminant Estimation.pdf

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Preview of Adaptive Language Modeling with Minimum Discriminant Estimation
🔗 Source: aclanthology.org
📊 Size: 280 KB
👤 Author: S. Della Pietra; V. Della Pietra; R. L. Mercer; S. Roukos
⬇️ Downloads: 29

Summary

The authors present an algorithm to adapt an n-gram language model to a document as it's dictated, improving its accuracy. Here's a concise summary:

- Problem: Static n-gram language models (SLM) have fixed probabilities, independent of the document being predicted, leading to suboptimal performance.
- Solution: Adapt the language model to match the current document more closely using a partially dictated document.
- Method: Estimate a unigram distribution for observed words, then find the closest n-gram distribution to the static one that satisfies the marginal constraints derived from the document, using minimum discrimination information (MDI) estimation.
- Algorithm: Alternating minimization, cycling through constraints until convergence.
- Application: Implemented for a bigram model (ME cache), reducing perplexity from 290 to 208 on a 321-word document.
- Previous Work: Similar approaches were used for part-of-speech models and trigram models, showing up to 25% lower recognition error rates.

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Description

Algorithm adapts n-gram language model to a dictating document, reducing perplexity from 290 to 208 on a 321-word document.

Technical Information

  • File Format: PDF
  • File Size: 280 KB
  • Pages: 4
  • Language: EN
  • Author: S. Della Pietra; V. Della Pietra; R. L. Mercer; S. Roukos
  • Total Downloads: 29
  • Last Updated: 2 hours ago

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This PDF document about Adaptive Language Modeling with Minimum Discriminant Estimation provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Adaptive Language Modeling with Minimum Discriminant Estimation.

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