Although the performance of SMT systems has improved over a range of different linguistic phenomena, negation has not yet received adequate treatment. Previous works have considered the problem of translating negative data as one of data sparsity (Wetzel and Bond (2012)) or of structural differences between source and target language with respect to the placement of negation (Collins et al. (2005)). This work starts instead from the questions of what is meant by negation and what makes a good translation of negation. These questions have led us to explore the use of semantics of negation in SMT — specifically, identifying core semantic elements of negation (cue, event and scope) in a source-side dependency parse and reranking hypotheses on the n-best list produced after decoding according to the extent to which an hypothesis realises these elements. The method shows considerable improvement over the baseline as measured by BLEU scores and Stanford’s entailmentbased MT evaluation metric (Pado et al.
['Federico Fancellu', 'Bonnie Webber']
Applying the semantics of negation to SMT through n-best list re-ranking