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RTM-DCU: Predicting semantic similarity with referential translation machines

Bicici, Ergun (2015) RTM-DCU: Predicting semantic similarity with referential translation machines. In: SemEval-2015: Semantic Evaluation Exercises - International Workshop on Semantic Evaluation, 4-5 June 2015, Denver, CO. USA.

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.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Machine translating
Computer Science > Machine learning
Computer Science > Artificial intelligence
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Nakov, Preslav, Zesch, Torsten, Cer, Daniel and Jurgens, David, (eds.) Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). . ACL Anthology.
Publisher:ACL Anthology
Official URL:http://dx.doi.org/10.3115/v1/S14-2085
Copyright Information:© 2015 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI (13/TIDA/I2740) for the project ``Monolingual and Bilingual Text Quality Judgments with Translation Performance Prediction'', SFI (12/CE/I2267) as part of the ADAPT CNGL Centre for Global Intelligent Content
ID Code:20650
Deposited On:16 Jun 2015 09:30 by Mehmet Ergun Bicici . Last Modified 22 Jul 2019 13:51

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