Barzegar, Siamak and Davis, Brian and Handschuh, Siegfried and Freitas, Andre (2018) Multilingual Semantic Relatedness using lightweight machine translation. In: : 2018 IEEE 12th International Conference on Semantic Computing (ICSC). IEEE. ISBN 9781538644089

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Abstract

Distributional semantic models are strongly dependent on the size and the quality of the reference corpora, which embeds the commonsense knowledge necessary to build comprehensive models. While high-quality texts containing largescale commonsense information are present in English, such as Wikipedia, other languages may lack sufficient textual support to build distributional models. This paper proposes using the combination of a lightweight (sloppy) machine translation model and an English Distributional Semantic Model (DSM) to provide higher quality word vectors for languages other than English. Results show that the lightweight MT model introduces significant improvements when compared to language-specific distributional models. Additionally, the lightweight MT outperforms more complex MT methods for the task of word-pair translation.

Item Type:	Book Section
Additional Information:	This publication has emanated from research funded in part from the European Unions Horizon 2020 research and innovation programme under grant agreement No 645425 SSIX and Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289
Keywords:	Multilingual Distributional Semantic Models; Machine Translation; Semantic Similarity; Semantic Relatedness;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13398
Identification Number:	https://doi.org/10.1109/ICSC.2018.00024
Depositing User:	Brian Davis
Date Deposited:	05 Oct 2020 15:39
Publisher:	IEEE
Refereed:	Yes
Funders:	European Union Horizon 2020 programme, Science Foundation Ireland (SFI)
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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