RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines

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. 1 Referential Translation Machine (RTM) We present positive results from a fully automated judge for semantic similarity based on Referential Translation Machines (Biçici and Way, 2014b) in two semantic similarity tasks at SemEval-2015, Semantic Evaluation Exercises International Workshop on Semantic Evaluation (Nakov et al., 2015). Referential translation machine (RTM) is a computational model for identifying the acts of translation for translating between any given two data sets with respect to a reference corpus selected in the same domain. An RTM model is based on the selection of interpretants, training data close to both the training set and the test set, which allow shared semantics by providing context for similarity judgments. Each RTM model is a data translation and translation prediction model between the instances in the training set and the test set and translation acts are indicators of the data transformation and translation. RTMs present an accurate and language independent solution for making semantic similarity judgments. RTMs pioneer a computational model for quality and semantic similarity judgments in monolingual and bilingual settings using retrieval of relevant training data (Biçici and Yuret, 2015) as interpretants for reaching shared semantics. RTMs achieve (i) top performance when predicting the quality of translations (Biçici, 2013; Biçici and Way, 2014a); (ii) top performance when predicting monolingual cross-level semantic similarity; (iii) second performance when predicting paraphrase and semantic similarity in Twitter (iv) good performance when judging the semantic similarity of sentences; (iv) good performance when evaluating the semantic relatedness of sentences and their entailment (Biçici and Way, 2014b). RTMs use Machine Translation Performance Prediction (MTPP) System (Biçici et al., 2013; Biçici and Way, 2014b), which is a state-of-the-art (SoA) performance predictor of translation even without using the translation. MTPP system measures the coverage of individual test sentence features found in the training set and derives indicators of the closeness of test sentences to the available training data, the difficulty of translating the sentence, and the presence of acts of translation for data transformation. MTPP features for translation acts are provided in (Biçici and Way, 2014b). RTMs become the 2nd system out of 13 systems participating in Paraphrase and Semantic Similarity in Twitter (Task 1) (Xu et al., 2015) and achieve good results in Semantic Tex-