Search Results for author: Nikola Mrkšić

Found 20 papers, 9 papers with code

Training Neural Response Selection for Task-Oriented Dialogue Systems

1 code implementation ACL 2019 Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks.

Chatbot Language Modelling +1

Fully Statistical Neural Belief Tracking

1 code implementation29 May 2018 Nikola Mrkšić, Ivan Vulić

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).

Dialogue State Tracking

Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources

1 code implementation NAACL 2018 Ivan Vulić, Goran Glavaš, Nikola Mrkšić, Anna Korhonen

Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet.

Dialogue State Tracking Text Simplification +1

Specialising Word Vectors for Lexical Entailment

1 code implementation17 Oct 2017 Ivan Vulić, Nikola Mrkšić

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.

Lexical Entailment Semantic Similarity +1

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

no code implementations EMNLP 2017 Ivan Vulić, Nikola Mrkšić, Anna Korhonen

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines.

Cross-Lingual Transfer Feature Engineering +3

Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

no code implementations ACL 2017 Ivan Vulić, Nikola Mrkšić, Roi Reichart, Diarmuid Ó Séaghdha, Steve Young, Anna Korhonen

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures.

Dialogue State Tracking

Counter-fitting Word Vectors to Linguistic Constraints

2 code implementations NAACL 2016 Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Lina Rojas-Barahona, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.

Dialogue State Tracking Semantic Similarity +1

Multi-domain Dialog State Tracking using Recurrent Neural Networks

no code implementations IJCNLP 2015 Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young

Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind.

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