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.
Researcher in Computer Science and Engineering
PhD in Computer Engineering from
Department of Computer Engineering, Koç University.
June 22, 2015
RTM-DCU: Referential Translation Machines for Semantic Similarity
Ergun Biçici and Andy Way. RTM-DCU: Referential Translation Machines for Semantic Similarity. In SemEval-2014: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, Dublin, Ireland, 23-24 August 2014. [PDF ] Keyword(s): Machine Translation, Machine Learning, Quality Estimation, Semantic Similarity.
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 solution to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in the 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.
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 solution to all similarity tasks and achieve top performance when predicting monolingual cross-level semantic similarity (Task 3) and good results in the 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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