Combining word embeddings and convolutional neural networks to detect duplicated questions
This work addresses duplicate question detection for online platforms like Quora, but it is incremental as it builds on existing methods for paraphrase detection.
The paper tackled the problem of detecting semantically similar questions by combining word embeddings and CNNs, achieving competitive results on the Quora dataset with over 400k question pairs.
Detecting semantic similarities between sentences is still a challenge today due to the ambiguity of natural languages. In this work, we propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks (CNNs). In addition, we demonstrate how the cosine similarity metric can be used to effectively compare feature vectors. Our network is trained on the Quora dataset, which contains over 400k question pairs. We experiment with different embedding approaches such as Word2Vec, Fasttext, and Doc2Vec and investigate the effects these approaches have on model performance. Our model achieves competitive results on the Quora dataset and complements the well-established evidence that CNNs can be utilized for paraphrase detection tasks.