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We use referential translation machines (RTMs) for predicting the semantic similarity of text in both STS Core and Cross-lingual STS. 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 14th out of 26 submissions in Cross-lingual STS. We also present rankings of various prediction tasks using the performance of RTM in terms of MRAER, a normalized relative absolute error metric. 1 Semantic Agreement We participated in Semantic Textual Similarity task at SemEval-2016 (Bethard et al., 2016) with RTMs. RTMs identify translation acts between any two data sets with respect to interpretants, data close to the task instances, effectively judging monolingual and bilingual similarity. We use RTMs for predicting the semantic similarity of text. Interpretants are used to derive features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of the acts of translation, which may ubiquitously be observed in communication. Semantic Web’s dream is to allow machines to share, exploit, and understand knowledge on the web. As more and more shared conceptualizations of domains emerge, we get closer to this goal. Semantic textual similarity (STS) task (Agirre et al., 2016) at SemEval-2016 (Bethard et al., 2016) is about quantifying the degree of similarity between two given sentences S1 and S2 in the same language (English) in STS Core (STS English) or in different languages (English or Spanish) in Cross-lingual STS (STS Spanish), with a real number in [0, 5]. S1 and S2 may be constructed using different models and with different conceptualizations of the world or different ontologies and different vocabulary. Even if two instances are categorized as same, they may have different implications for commonsense reasoning (both albatros and penguin are a bird) (Biçici, 2002). The existence of a single ontology that can cover all the required conceptual information for reaching semantic understanding is questionable because it would presume an agreement among all ontology experts. Yet, semantic agreement using heterogeneous ontologies may not be possible as well since in the most extreme case, they would not use the same tokens. Therefore, semantic textual similarity is harder than the Chinese room thought experiment (Internet Encyclopedia of Philosophy, 2016) since we are not given any instructions about how to answer queries. Our goal is to quantify the level of semantic agreement between S1 and S2 and RTMs use interpretants, data close to the task instances for building prediction models for semantic similarity. 2 Referential Translation Machine Each RTM model is a data 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 are powerful enough to be applicable in different domains and tasks while achieving top performance in both

RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics