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Answer: Use data augmentation techniques to generate additional training samples with variations in wording and phrasing.
Using data augmentation techniques to generate additional training samples with variations in wording and phrasing can help the model better understand the nuances of user queries and improve its performance. Adding more random utterances (Option A) may not necessarily improve the model's understanding of the intents. Merging intents into a single intent (Option C) could result in loss of important information and reduce the model's accuracy. Fine-tuning the model's parameters (Option D) can be helpful, but it is not as effective as data augmentation in improving the model's ability to handle variations in user queries.
Author: LeetQuiz Editorial Team
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You are working on a project to develop a language understanding model for a virtual personal shopping assistant. The model should be able to handle various intents such as 'search_product', 'compare_products', and 'read_reviews'. You want to optimize the model to improve its performance. Which of the following techniques should you consider?
A
Increase the number of training samples for each intent by adding more random utterances.
B
Use data augmentation techniques to generate additional training samples with variations in wording and phrasing.
C
Remove some of the intents and merge them into a single intent to simplify the model.
D
Fine-tune the model's parameters to improve its ability to generalize from the training data.
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