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        <title>Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection
%A Poerner, Nina
%A Schütze, Hinrich
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F poerner-schutze-2019-multi
%X 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.
%R 10.18653/v1/D19-1173
%U https://aclanthology.org/D19-1173
%U https://doi.org/10.18653/v1/D19-1173
%P 1630-1641

Markdown (Informal)

[Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection](https://aclanthology.org/D19-1173) (Poerner & Schütze, EMNLP-IJCNLP 2019)

• Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (Poerner & Schütze, EMNLP-IJCNLP 2019)

ACL

• Nina Poerner and Hinrich Schütze. 2019. Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1630–1641, Hong Kong, China. Association for Computational Linguistics.