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Title:CNGL-CORE: Referential translation machines for measuring semantic similarity
Authors:Ergun Bicici and Josef van Genabith, 2013
Abstract: We invent referential translation machines (RTMs), a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for judging the semantic similarity between text. RTMs make quality and semantic similarity judgments possible by using retrieved relevant training data as interpretants for reaching shared semantics. An MTPP (machine translation performance predictor) model derives features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of acts of translation involved. We view semantic similarity as paraphrasing between any two given texts. Each view is modeled by an RTM model, giving us a new perspective on the bi- nary relationship between the two. Our prediction model is the 15th on some tasks and 30th overall out of 89 submissions in total ac- cording to the official results of the Semantic Textual Similarity (STS 2013) challenge.
ICHEC Project:Large Scale Experiments on the Prediction of Machine Translation Performance
Publication:*SEM 2013: The First Joint Conference on Lexical and Computational Semantics, Atlanta, Georgia, USA, 13-14 June
URL: http://clic.cimec.unitn.it/starsem2013-program/76_Paper.pdf
Status: Published

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