Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model

Recent work has shown that Tree Ker- nels (TKs) and Convolutional Neural Net- works (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vec- tor Machines (SVMs) is promising as it can exploit both the syntactic patterns cap- tured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hy- brid approach combining preference rank- ing applied to TKs and pointwise rank- ing applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.