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In this work, we retrained the distilled BERT language model for Walmart'svoice shopping assistant on retail domain-specific data. We also injecteduniversal syntactic dependencies to improve the performance of the modelfurther. The Natural Language Understanding (NLU) components of the voiceassistants available today are heavily dependent on language models for varioustasks. The generic language models such as BERT and RoBERTa are useful fordomain-independent assistants but have limitations when they cater to aspecific domain. For example, in the shopping domain, the token 'horizon' meansa brand instead of its literal meaning. Generic models are not able to capturesuch subtleties. So, in this work, we retrained a distilled version of the BERTlanguage model on retail domain-specific data for Walmart's voice shoppingassistant. We also included universal dependency-based features in theretraining process further to improve the performance of the model ondownstream tasks. We evaluated the performance of the retrained language modelon four downstream tasks, including intent-entity detection, sentimentanalysis, voice title shortening and proactive intent suggestion. We observedan increase in the performance of all the downstream tasks of up to 1.31% onaverage.

Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies