This is the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions
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Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e. g., question similarity, is still an open problem.
On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database.
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data.
We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines.
In this paper we propose a system for reranking answers for a given question.