| 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. |
Similar Papers
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
Copied to clipboard
| 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. |
Composition-contrastive Learning for Sentence Embeddings (2023.acl-long)
Copied to clipboard
| 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. |
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for few-shot sentence embeddings are not robust enough to measure sentence similarity due to the ambiguity and variability of linguistic expressions. |
| Approach: | They propose a mutual information-based contrastive learning framework that imposes alignment between different views during contrastive training. |
| Outcome: | The proposed framework shows strong performance in few-shot learning domain compared to state-of-the-art methods, but comparable in full-shot scenario. |
Contrasting distinct structured views to learn sentence embeddings (2021.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods to build sentence embeddings rely on a similar Recurrent Neural Network (RNN) heterogeneity of performances across models and tasks makes us assume some structures might be better adapted given the considered task or sentence. |
| Approach: | They propose a self-supervised method that builds sentence embeddings from syntactic structures . they hypothesize that some linguistic representations might be better adapted given the task . |
| Outcome: | The proposed method outperforms comparable methods on several tasks from standard sentence embedding benchmarks. |
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings (2023.acl-long)
Copied to clipboard
| 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. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-66)
Copied to clipboard
| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
Copied to clipboard
| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Improving Multi-lingual Alignment Through Soft Contrastive Learning (2024.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods to train multi-lingual sentence embeddings ruins the mono-lingual space. |
| Approach: | They propose a method to align multi-lingual embeddings based on similarity of sentences measured by a pre-trained mono-lingual teacher model. |
| Outcome: | The proposed method outperforms existing multi-lingual embeddings including LaBSE on five languages and on a translation pair for Tatoeba dataset. |
RobustEmbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing PLMs suffer from poor robustness in adversarial scenarios, despite their success with unseen samples. |
| Approach: | They propose a self-supervised sentence embedding framework that enhances generalization and robustness in various text representation tasks and against diverse adversarial attacks. |
| Outcome: | The proposed framework improves generalization and robustness in various representation tasks and against diverse adversarial attacks. |
Contrastive Learning of Sentence Embeddings from Scratch (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to learn sentence embeddings with unlabeled data are limited due to copyright restrictions, data distribution issues, and messy formats. |
| Approach: | They propose a contrastive learning framework that trains sentence embeddings with synthetic data. |
| Outcome: | The proposed framework produces positive and negative annotations given unlabeled sentences and generates sentences along with their corresponding annotations from scratch. |