Learning question similarity in CQA from references and query-logs

Community question answering (CQA) sites are quickly becoming an invaluable source of information in many domains. Since CQA forums are based on the contributions of many authors, the problem of finding similar or even duplicate questions is essential. In the absence of supervised data for this problem, we propose a novel approach to generate weak labels based on easily obtainable data that exist in most CQAs, e.g., query logs and references in the answers. These labels accommodate training of auxiliary supervised text classification models. The internal states of these models serve as meaningful question representations and are used for semantic similarity. We demonstrate that these methods are superior to state of the art text embedding methods for the question similarity task.