WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach (2021.findings-emnlp)
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| Challenge: | Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data. |
| Approach: | They conduct a thorough examination of pretrained model based unsupervised sentence embeddings. |
| Outcome: | The proposed approach improves on whitening-based vector normalization with less than 10 lines of code. |
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
| Outcome: | The proposed method achieves 3.7% to 6.4% Pearson’s r over single-source word embeddings on the STS Benchmark and on the StS12-STS16 datasets. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
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Generating Datasets with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Recent approaches to obtain high-quality sentence embeddings from pretrained language models require labeled data or finetuned on large set of labeles. |
| Approach: | They propose to use generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch and fine tune much smaller and more efficient models. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
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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More Discriminative Sentence Embeddings via Semantic Graph Smoothing (2024.eacl-short)
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| Challenge: | Text categorization is a natural language processing task that involves arranging texts into coherent groups based on their content. |
| Approach: | They propose to use semantic graph smoothing to enhance sentence embeddings from pretrained models to improve results for supervised and unsupervised document categorization tasks. |
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Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (N18-1)
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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. |
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On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
| Approach: | They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective. |
| Outcome: | The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks. |
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability. |
| Approach: | They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information. |
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Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models (2022.lrec-1)
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| Challenge: | Existing methods for creating metaembeddings from static word embeddings have been proposed, but they are not tied to a particular downstream task. |
| Approach: | They propose a sentence-level meta-embedding learning method that takes contextualised word embedding models and learns a phrase embeddable that preserves complementary strengths of the input source NLMs. |
| Outcome: | The proposed method outperforms existing methods on semantic textual similarity benchmarks on a supervised baseline and on token-level embeddings. |