activity
20172022
most citedTraining Neural Response Selection for Task-Oriented Dialogue Systems

55 citations · 94 across the 14 of their papers we have counts for

collaborators

23 papers

cs.CL20222 cited

NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue

Iñigo Casanueva, Ivan Vulić, Georgios P. Spithourakis +1

We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation envir…

cs.CL2022

EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification

Georgios P. Spithourakis, Ivan Vulić, Michał Lis +2

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E),…

cs.CL20225 cited

Improved and Efficient Conversational Slot Labeling through Question Answering

Gabor Fuisz, Ivan Vulić, Samuel Gibbons +2

Transformer-based pretrained language models (PLMs) offer unmatched performance across the majority of natural language understanding (NLU) tasks, including a body of question answ…

cs.CL20211 cited

ConvFiT: Conversational Fine-Tuning of Pretrained Language Models

Ivan Vulić, Pei-Hao Su, Sam Coope +5

Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence enco…

cs.CL2019

Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling

Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski +3

Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels. However,…

cs.CL20191 cited

Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation

Bo-Hsiang Tseng, Paweł Budzianowski, Yen-Chen Wu +1

Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain. Thi…