ALTN: Word Alignment Features for Cross-lingual Textual Entailment
Marco Turchi, Matteo Negri · 2013
We present a supervised learning approach to cross-lingual textual entailment that explores statistical word alignment models to predict entailment relations between sentences written 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%). 1