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Hypotheses selection for re-ranking semantic annotations

Discriminative reranking has been successfully used for several tasks of Natural Language Processing (NLP). Recently it has been applied also to Spoken Language Understanding, improving state-of-the-art for some applications. However, such proposed models can be further improved by considering: (i) a better selection of the initial n-best hypotheses to be re-ranked and (ii) the use of a strategy that decides when the reranking model should be used, i.e. in some cases only the basic approach should be applied.