| Challenge: | Recent work shows potential to learn vector representations from unlabelled data without task-specific fine-tuning. |
| Approach: | They propose to maximize alignment between textual embeddings and a composition of their phrasal constituents. |
| Outcome: | The proposed approach improves on similarity tasks comparable to state-of-the-art approaches. |
Similar Papers
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations (2021.acl-long)
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| Challenge: | Sentence embeddings are an important component of many natural language processing systems. |
| Approach: | They propose a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. |
| Outcome: | The proposed approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. |
Learning High-Quality and General-Purpose Phrase Representations (2024.findings-eacl)
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| Challenge: | Pre-trained language models for phrasal embeddings are unnecessarily complex and require to be pre-tuned on a corpus with context sentences. |
| Approach: | They propose a framework to learn phrase representations in a context-free fashion. |
| Outcome: | The proposed framework generates superior phrase embeddings while requiring a smaller model size. |
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval (2023.acl-long)
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| Challenge: | Contrastive learning is the dominant paradigm for learning text representations from parallel text, but finding negative examples can be expensive in terms of compute or manual effort. |
| Approach: | They propose a generative model for learning multilingual text embeddings which encourages source separation in multilingual contexts by an approximation. |
| Outcome: | The proposed model outperforms both a strong contrastive and generative baseline on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval. |
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. |
| Outcome: | The proposed method outperforms state-of-the-art models on most benchmark tasks and is robust to the produced general-purpose sentence embeddings. |
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings (2023.acl-long)
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| Challenge: | Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art. |
| Approach: | They propose a whitening-based contrastive learning method for sentence embedding learning which combines contrastive and shuffled group whitening. |
| Outcome: | The proposed method achieves better alignment and uniformity on seven semantic textual similarity tasks. |
MCSE: Multimodal Contrastive Learning of Sentence Embeddings (2022.naacl-main)
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| Challenge: | Existing approaches to learning semantically meaningful sentence embeddings are limited by the complexity of pre-trained models. |
| Approach: | They propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective. |
| Outcome: | The proposed approach improves the state-of-the-art average Spearman’s correlation by 1.7% on a variety of semantic textual similarity tasks. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
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| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |