11 citations · 12 across the 3 of their papers we have counts for
6 papers
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey
Samuel Louvan, Bernardo Magnini
In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and…
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification
Samuel Louvan, Bernardo Magnini
Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domain…
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm, Katrin Marheinecke, Stefanie Hegele +44
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cr…
Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems
Vevake Balaraman, Bernardo Magnini
In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST appr…
A Robust Data-Driven Approach for Dialogue State Tracking of Unseen Slot Values
Vevake Balaraman, Bernardo Magnini
A Dialogue State Tracker is a key component in dialogue systems which estimates the beliefs of possible user goals at each dialogue turn. Deep learning approaches using recurrent n…
Scalable Neural Dialogue State Tracking
Vevake Balaraman, Bernardo Magnini
A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn. Most of the current DST tracke…