1 code implementation • ACL 2018 • Nikola Mrk{\v{s}}i{\'c}, Ivan Vuli{\'c}
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).
no code implementations • NAACL 2018 • Pei-Hao Su, Nikola Mrk{\v{s}}i{\'c}, I{\~n}igo Casanueva, Ivan Vuli{\'c}
The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems.
1 code implementation • NAACL 2018 • Ivan Vuli{\'c}, Nikola Mrk{\v{s}}i{\'c}
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation.
Ranked #1 on Lexical Entailment on HyperLex
no code implementations • EACL 2017 • Ivan Vuli{\'c}, Nikola Mrk{\v{s}}i{\'c}, Mohammad Taher Pilehvar
Specialising vector spaces to maximise their content with respect to one key property of vector space models (e. g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning.
no code implementations • TACL 2017 • Nikola Mrk{\v{s}}i{\'c}, Ivan Vuli{\'c}, Diarmuid {\'O} S{\'e}aghdha, Ira Leviant, Roi Reichart, Milica Ga{\v{s}}i{\'c}, Anna Korhonen, Steve Young
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.