Paper: SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT

ACL ID S13-2006
Title SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in av- erage. This system consists of a SVM clas- sifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft cardinal- ity. Furthermore, this system was simplified by providing a single model for the 4 pairs of languages obtaining better (unofficial) re- sults than separate models for each language pair. We also evaluated the use of additional circular-pivoting translations achieving results 6.14% above the best official results.