Search Results for author: Nikola Mrk{\v{s}}i{\'c}

Found 6 papers, 2 papers with code

Fully Statistical Neural Belief Tracking

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).

Dialogue Management Dialogue State Tracking +2

Deep Learning for Conversational AI

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.

Decision Making Dialogue Management +5

Specialising Word Vectors for Lexical Entailment

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.

Dialogue State Tracking Lexical Entailment +8

Word Vector Space Specialisation

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.

Lexical Entailment Representation Learning +2

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