We use referential translation machines (RTMs) for predicting the semantic similarity of text.
RTMs are a computational model for identifying the
translation acts between any two data sets with respect to interpretants selected in the same domain,
which are effective when making monolingual and bilingual similarity judgments.
RTMs judge the quality or the semantic similarity of text by using retrieved relevant training data as interpretants for reaching shared semantics.
We derive features measuring the closeness of the test sentences to the training data via interpretants, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication.
RTMs provide a language independent approach to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in semantic relatedness and entailment (Task 1) and multilingual semantic textual similarity (STS) (Task 10). RTMs remove the need to access any task or domain specific information or resource.
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Funders:
CNGL Centre for Next Generation Localisation, Dublin City University, Science Foundation Ireland (13/TIDA/I2740) for the project ``Monolingual and Bilingual Text Quality Judgments with Translation Performance Prediction'', European Commission through the QTLaunchPad FP7 project (No: 296347)
ID Code:
20236
Deposited On:
30 Sep 2014 09:51 by
Mehmet Ergun Bicici
. Last Modified 09 Nov 2018 14:21