Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Open accessAbstract
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful.Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted.In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors (Kiros et al., 2015) on a wide range of transfer tasks.Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks.Our encoder is publicly available 1 .
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Previous work found that the SNLI datasets are suitable for training sentence embeddings.
“Previous work (Conneau et al., 2017; Cer et al., 2018) found that the SNLI datasets are suitable for training sentence embeddings.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
InferSent uses labeled data of the Stanford Natural Language Inference dataset and the MultiGenre NLI dataset to train a siamese BiLSTM network with max-pooling over the output.
“InferSent (Conneau et al., 2017) uses labeled data of the Stanford Natural Language Inference dataset (Bowman et al., 2015) and the MultiGenre NLI dataset (Williams et al., 2018) to train a siamese BiLSTM network with max-pooling over the output.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
Fine-tuning SBERT on NLI data creates sentence embeddings that significantly outperform other state-of-the-art sentence embedding methods like InferSent and Universal Sentence Encoder.
“We fine-tune SBERT on NLI data, which creates sentence embeddings that significantly outperform other state-of-the-art sentence embedding methods like InferSent (Conneau et al., 2017) and Universal Sentence Encoder (Cer et al., 2018).”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
Conneau et al. showed that InferSent consistently outperforms unsupervised methods like SkipThought.
“Conneau et al. showed, that InferSent consistently outperforms unsupervised methods like SkipThought.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
Conneau et al. (2017) found it beneficial for the BiLSTM-layer of InferSent to use MAX instead of MEAN pooling.
“This is in contrast to (Conneau et al., 2017), who found it beneficial for the BiLSTM-layer of InferSent to use MAX instead of MEAN pooling.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
InferSent and Universal Sentence Encoder both use (u, v, |u-v|, u*v) as input for a softmax classifier.
“InferSent (Conneau et al., 2017) and Universal Sentence Encoder (Cer et al., 2018) both use ( u, v, | u -v | , u ∗ v ) as input for a softmax classifier.”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Conneau et al., 2017
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“, 2018), bi-encoder networks (Conneau et al., 2017) and BERT-based methods (Reimers & Gurevych, 2019).”
- Authors
- Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A.
- Affiliations
- Meta (Israel) (IL)
- University of Cambridge (GB)
- Meta (United States) (US)
- Le Mans Université (FR)
- ORCID
- Barrault, Loïc
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 670-680
- OpenAlex
- W2612953412
- Cited by
- 2,049
- References
- 32
- Citations by year
- 2026: 31 · 2025: 76 · 2024: 113 · 2023: 213 · 2022: 221 · 2021: 335 · 2020: 392 · 2019: 427 · 2018: 224 · 2017: 16 · 2012: 1
- FWCI
- 132.10
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- natural language inference, supervised learning, transfer learning, sentence embeddings, word embeddings
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