Learning Distributed Representations of Sentences from Unlabelled Data
Open accessAbstract
Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-linear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.
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Hill et al. (2016) showed that the task on which sentence embeddings are trained significantly impacts their quality.
“Hill et al. (2016) showed, that the task on which sentence embeddings are trained significantly impacts their quality.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Hill et al., 2016
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
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“, 2011; 2013), Sequential Denoising Autoencoders (SDAE), FastSent (Hill et al., 2016), etc.”
BackgroundMethodInfluentialLearned in Translation: Contextualized Word Vectors
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“Hill et al. [2016] study the effect of transferring from a variety of source domains to the semantic similarity tasks in Agirre et al. [2014].”
BackgroundDeterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
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Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., & Blunsom, P. (2018) · arXiv · doi:10.48550/arxiv.1812.01193 · Cited by 367 · see citing works
“Traditional approaches make use of very large unsupervised datasets [17, 14], taking weeks to train.”
BackgroundDeep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350 · see citing works
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Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330 · see citing works
“Hill et al. (2016) also raises concern about current evaluation tasks in their recent work which provides a thorough survey of architectures and objectives for learning unsupervised sentence representations - including the above mentioned skip-thoughts.”
BackgroundParameter-Efficient Transfer Learning with Diff Pruning
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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
- Authors
- Hill, F., Cho, K., & Korhonen, A.
- Affiliations
- University of Cambridge (GB)
- New York University (US)
- Venue
- Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2016
- Pages
- 1367-1377
- OpenAlex
- W2271328876
- Cited by
- 566
- References
- 3
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 19 · 2023: 47 · 2022: 52 · 2021: 76 · 2020: 75 · 2019: 108 · 2018: 116 · 2017: 38 · 2016: 17
- FWCI
- 50.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- distributed representations, sentence embeddings, phrase representation, unsupervised learning, word embeddings, log-linear models, domain portability, model complexity
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