no code implementations • SIGDIAL (ACL) 2021 • Vevake Balaraman, Seyedmostafa Sheikhalishahi, Bernardo Magnini
This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems.
no code implementations • IWSLT 2016 • M. Amin Farajian, Rajen Chatterjee, Costanza Conforti, Shahab Jalalvand, Vevake Balaraman, Mattia A. Di Gangi, Duygu Ataman, Marco Turchi, Matteo Negri, Marcello Federico
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
1 code implementation • 31 Jul 2023 • Vevake Balaraman, Arash Eshghi, Ioannis Konstas, Ioannis Papaioannou
We demonstrate the usefulness of the data by training and evaluating strong baseline models for executing TPRs.
1 code implementation • 21 Jan 2020 • 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.
no code implementations • 1 Nov 2019 • Vevake Balaraman, Bernardo Magnini
This makes extending the candidate list for a slot without model retaining infeasible and also has limitations in modelling for low resource domains where training data for slot values are expensive.
1 code implementation • 22 Oct 2019 • 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.
Ranked #6 on Dialogue State Tracking on Wizard-of-Oz
2 code implementations • 2 Oct 2019 • Seyedmostafa Sheikhalishahi, Vevake Balaraman, Venet Osmani
This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model.
no code implementations • WS 2018 • Marco Guerini, Simone Magnolini, Vevake Balaraman, Bernardo Magnini
We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time.
no code implementations • 20 Sep 2017 • Simon Razniewski, Vevake Balaraman, Werner Nutt
In this work, we have developed a human-annotated dataset of 350 preference judgments among pairs of knowledge base properties for fixed entities.