Hypotheses Selection Criteria in a Reranking Framework for Spoken Language Understanding

Reranking models have been successfully applied to many tasks of Natural Language Processing. However, there are two aspects of this approach that need a deeper investigation: (i) Assessment of hypotheses generated for reranking at classification phase: baseline models generate a list of hypotheses and these are used for reranking without any assessment; (ii) Detection of cases where reranking models provide a worst result: the best hypothesis provided by the reranking model is assumed to be always the best result. In some cases the reranking model provides an incorrect hypothesis while the baseline best hypothesis is correct, especially when baseline models are accurate. In this paper we propose solutions for these two aspects: (i) a semantic inconsistency metric to select possibly more correct n-best hypotheses, from a large set generated by an SLU basiline model. The selected hypotheses are reranked applying a state-of-the-art model based on Partial Tree Kernels, which encode SLU hypotheses in Support Vector Machines with complex structured features; (ii) finally, we apply a decision strategy, based on confidence values, to select the final hypothesis between the first ranked hypothesis provided by the baseline SLU model and the first ranked hypothesis provided by the re-ranker. We show the effectiveness of these solutions presenting comparative results obtained reranking hypotheses generated by a very accurate Conditional Random Field model. We evaluate our approach on the French MEDIA corpus. The results show significant improvements with respect to current state-of-the-art and previous re-ranking models.