Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection

By Nina Poerner, Hinrich Schütze. 2019

15 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2019, methodology): “The latter method was also used by Poerner and Sch¨utze (2019) for domain-speciﬁc Duplicate Question Detection.”