Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification

• URL: http://arxiv.org/abs/2007.11314v1
• Date: Wed, 22 Jul 2020 10:09:26 GMT
• Title: Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification
• Authors: Nicole Peinelt, Dong Nguyen and Maria Liakata
• Abstract summary: We propose two ways of merging topics with word embeddings in a new neural architecture for question paraphrase identification. Our results show that our system outperforms neural baselines on multiple CQA datasets.
• Score: 24.574227630018758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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