This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a softcosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B.

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The approach proposed is an alternative to the traditional architecture of Spoken Dialogue Systems where the system belief is either not taken into account during the Automatic Speech Recognition process or included in the decoding process but never challenged. By representing all the conceptual structures handled by the Dialogue Manager by Finite State Machines and by building a conceptual model that contains all the possible interpretations of a given wordgraph, we propose a decoding architecture that searches first for the best conceptual interpretation before looking for the best string of words. Once both N-best sets (at the concept level and at the word level) are generated, a verification process is performed on each N-best set using acoustic and linguistic confidence measures. A first selection strategy that does not include for the moment the Dialogue context is proposed and significant error reduction on the understanding measures are obtained.