RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines

Ergun Biçici. RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines. In SemEval-2015: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, Denver, Colorado, USA, June 2015. [WWW] Keyword(s): Machine Translation, Machine Learning, Performance Prediction, Semantic Similarity.

We use referential translation machines (RTMs) for predicting the semantic similarity of text. RTMs are a computational model effectively judging monolingual and bilingual similarity while identifying translation acts between any two data sets with respect to interpretants. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. RTMs become the 2nd system out of 13 systems participating in Paraphrase and Semantic Similarity in Twitter, 6th out of 16 submissions in Semantic Textual Similarity Spanish, and 50th out of 73 submissions in Semantic Textual Similarity English.