Evaluating Compound Splitters Extrinsically with Textual Entailment
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 taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.
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Information RetrievalMachine TranslationNatural Language InferenceSpeech RecognitionTranslationSimilar Papers 제목 키워드 기반
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