Search Results for author:
Found 5 papers, 0 papers with code
Every time I fire a conversational designer, the performance of the dialogue system goes down
In fact, rules from conversational designers used in CLINN significantly outperform a state-of-the-art neural-based dialogue system when trained with smaller sets of annotated dialogues.
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation
Our results indicate a significant performance drop in GPT-3. 5 on the unfamiliar Termite dataset, even with ATD modifications, highlighting the effect of Data Contamination on LLMs in Text-to-SQL translation tasks.
Every time I fire a conversational designer, the performance of the dialog system goes down
Incorporating explicit domain knowledge into neural-based task-oriented dialogue systems is an effective way to reduce the need of large sets of annotated dialogues.
Almawave-SLU: A new dataset for SLU in Italian
The widespread use of conversational and question answering systems made it necessary to improve the performances of speaker intent detection and understanding of related semantic slots, i. e., Spoken Language Understanding (SLU).
Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
Moreover, we annotated a new dataset for the Italian language, and we observed similar performances without the need for changing the model.
