Multi View Domain Adapted Sentence Embeddings for Low Resource Unsupervised Duplicate Question Detection
We address the problem of Duplicate Question Detection (DQD) in low resource domain specific Community Question Answering forums. Our multi view framework MV DASE combines an ensemble of sentence encoders via Generalized Canonical Correlation Analysis, using unlabeled data only. In our experiments, the ensemble includes generic and domain specific averaged word embeddings, domain finetuned BERT and the Universal Sentence Encoder. We evaluate MV DASE on the CQADupStack corpus and on additional low resource Stack Exchange forums. Combining the strengths of different encoders, we significantly outperform BM25, all single view systems as well as a recent supervised domain adversarial DQD method.
EMNLP/IJCNLP