# CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity

## TL;DR

This paper describes CompiLIG's systems for cross-language semantic textual similarity, combining multiple methods to accurately estimate sentence similarity between Spanish and English, achieving top performance at SemEval-2017.

## Contribution

The paper introduces a hybrid approach combining syntax, dictionary, context, and machine translation methods for cross-language semantic similarity detection.

## Key findings

- Achieved 83.02% correlation with human annotations
- Ranked 1st in SemEval-2017 Track 4a
- Demonstrated effectiveness of combined unsupervised and supervised methods

## Abstract

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.