Evaluating compound splitters extrinsically with textual entailment

Abstract

Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by task-internal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.