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Related Questions
- How do subword tokenization techniques help to improve robustness to out-of-vocabulary words or phrases in conversational AI models?
- Can you discuss the role of language modeling objectives, such as masked language modeling, in enhancing robustness to out-of-vocabulary words or phrases?
- What is the impact of using character-level embeddings or subword-level embeddings on the robustness of conversational AI models to out-of-vocabulary words or phrases?
- How do knowledge graph-based methods, such as entity-centric language models, contribute to improving robustness to out-of-vocabulary words or phrases in conversational AI models?
- What are some strategies for augmenting the training data of conversational AI models with out-of-vocabulary words or phrases to improve their robustness?
- Can you explain the trade-offs between model size and robustness to out-of-vocabulary words or phrases in conversational AI models?
- How do techniques like transfer learning, fine-tuning, or multi-task learning help to improve the robustness of conversational AI models to out-of-vocabulary words or phrases?
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