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Found 6 papers, 1 papers with code
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction Genre
We also compared the classification accuracy of the logistic regression model with two deep-learning models.
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies
The generic language models such as BERT and RoBERTa are useful for domain-independent assistants but have limitations when they cater to a specific domain.
Improving Intent Classification in an E-commerce Voice Assistant by Using Inter-Utterance Context
So, it is important for the assistant to take into account the context from the user{'}s first utterance to understand her intention in the second one.
Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge
The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011.
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5. 53{\%} and 7. 7{\%} respectively.
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