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Found 6 papers, 2 papers with code
Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Current virtual assistant (VA) platforms are beholden to the limited number of languages they support.
Practical token pruning for foundation models in few-shot conversational virtual assistant systems
The VA system is expected to be a cost-efficient SaaS service with low training and inference time while achieving high accuracy even with a small number of training samples.
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants
The fine-tuning data is prepared carefully to cover a wide variety of slot-filling task scenarios that the model is expected to face across various domains.
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy.
Multilingual BERT Post-Pretraining Alignment
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models.
