Search Results for author: Diarmuid Ó Séaghdha

Found 8 papers, 2 papers with code

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 MORPH

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

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