| Challenge: | Recent studies show that averaging word embeddings is effective for NLP but these models represent a sentence only in terms of features of words or uni-grams. |
| Approach: | They propose a CNN-based model that uses both features of words and n-grams to encode sentences. |
| Outcome: | The proposed model performs better than existing models in transfer learning setting and exceeds state of the art in supervised learning setting by initializing the parameters with the pre-trained sentence embeddings. |
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| Challenge: | Currently, unsupervised word embeddings are routinely trained on large amounts of raw text data. |
| Approach: | They propose to use unsupervised word embeddings to train distributed representations of sentences. |
| Outcome: | The proposed method outperforms state-of-the-art models on most benchmark tasks and is robust to the produced general-purpose sentence embeddings. |
Universal Sentence Encoder for English (D18-2)
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil
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Static Word Embeddings for Sentence Semantic Representation (2025.emnlp-main)
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| Challenge: | Existing methods to learn fixed-length embeddings for sentence semantics require large computational cost, making it difficult to process billions of sentences cost-efficiently or deploy models on resource-constrained devices such as smartphones. |
| Approach: | They propose to extract word embeddings from a pre-trained Sentence Transformer and improve them with sentence-level principal component analysis followed by knowledge distillation or contrastive learning. |
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Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
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Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
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Towards Lossless Encoding of Sentences (P19-1)
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| Challenge: | Existing methods for encoding text into lossless representations focus on performing well on downstream tasks and are unable to reconstruct original sequence from learned embedding. |
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Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
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Unsupervised Sentence-embeddings by Manifold Approximation and Projection (2021.eacl-main)
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| Challenge: | Existing methods to generate sentence-embeddings are task-agnostic and often lose information because of word-order. |
| Approach: | They propose to generate sentence-embeddings by projecting sentences onto a fixed-dimensional manifold with the objective of preserving local neighbourhoods in the original space. |
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Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
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| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
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GASE: Generatively Augmented Sentence Encoding (2025.findings-emnlp)
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| Challenge: | Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarizing, or extracting keywords, followed by pooling the original and synthetic embeddings. |
| Approach: | They propose a training-free approach to improve sentence embeddings by applying generative text models for data augmentation at inference time. |
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