We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences writ- ten in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) organized within Semeval 2013 (Negri et al., 2013). The four runs submitted, one for each language combination covered by the test data (i.e. Spanish/English, German/English, French/English and Italian/English), achieved encouraging results. In terms of accuracy, performance ranges from 38.8% (for German/English) to 43.2% (for Italian/English). On the Italian/English and Spanish/English test sets our systems ranked second among five participants, close to the top results (respectively 43.4% and 45.4%).
ALTN: Word Alignment Features for Cross-Lingual Textual Entailment.
Turchi, Marco;Negri, Matteo
2013-01-01
Abstract
We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences writ- ten in different languages. Our approach is language independent, and was used to participate in the CLTE task (Task#8) organized within Semeval 2013 (Negri et al., 2013). The four runs submitted, one for each language combination covered by the test data (i.e. Spanish/English, German/English, French/English and Italian/English), achieved encouraging results. In terms of accuracy, performance ranges from 38.8% (for German/English) to 43.2% (for Italian/English). On the Italian/English and Spanish/English test sets our systems ranked second among five participants, close to the top results (respectively 43.4% and 45.4%).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
