  Question paraphrase identification is a key task in Community Question
Answering (CQA) to determine if an incoming question has been previously asked.
Many current models use word embeddings to identify duplicate questions, but
the use of topic models in feature-engineered systems suggests that they can be
helpful for this task, too. We therefore propose two ways of merging topics
with word embeddings (early vs. late fusion) in a new neural architecture for
question paraphrase identification. Our results show that our system
outperforms neural baselines on multiple CQA datasets, while an ablation study
highlights the importance of topics and especially early topic-embedding fusion
in our architecture.
