CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
By Jérémy Ferrero, Laurent Besacier, Didier Schwab, Frédéric Agnès. 2017
16 citations recorded by the source corpus.
We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based, dictionary-based, context-based, and MT-based methods. We also combine these methods in unsupervised and supervised way. Our best run ranked 1st on track 4a with a correlation of 83.02% with human annotations.
Subfields: Artificial Intelligence, Natural Language Processing
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In Artificial Intelligence
- Daniel Matthew Cer
- Graham Neubig
- Iryna Gurevych
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- Mona T. Diab
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In Natural Language Processing
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- Kawin Ethayarajh
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- Nils Reimers
Citations in the corpus
- SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “CompiLIG (Ferrero et al., 2017) The best Spanish-English performance on SNLI sentences was achieved by CompiLIG using the following cross-lingual features: conceptual similarity using”