Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual Similarity (2024.emnlp-main)
Copied to clipboard
| Challenge: | Semantic Textual Similarity (STS) is a key indicator of the encoding capabilities of embedding models. |
| Approach: | They propose to use Pearson’s correlation coefficient as a loss function to refine model performance beyond contrastive learning to achieve a Spearman’s ceiling. |
| Outcome: | The proposed method surpasses state-of-the-art strategies with minimal amount of fine-grained annotated samples. |
Similar Papers
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that a pretrained language model can capture sentence similarity but there is no interpretation method for the sentence similarities. |
| Approach: | They propose a pretrained language model that captures sentence similarity between embeddings and a transport-based distance measure that leverages semantically-aligned token pairs. |
| Outcome: | The proposed framework outperforms baselines on both STS and interpretable-STS benchmarks and provides interpretation consistent with human judgement. |
Are ELECTRA’s Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity (2024.findings-emnlp)
Copied to clipboard
| Challenge: | ELECTRA's sentence embeddings are poorer than BERT's, resulting in a significant drop in performance for semantic textual similarity (STS). |
| Approach: | They propose to use a truncated model fine-tuning method to repair the embeddings by reducing the number of parameters and producing smaller embeddables. |
| Outcome: | The proposed method improves Spearman correlation coefficient by over 8 points while increasing parameter efficiency on the STS Benchmark. |
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. |
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Incorporating contrastive learning objectives in sentence representation learning has yielded significant improvements on many sentence-level NLP tasks. |
| Approach: | They aim to examine why contrastive learning works for learning sentence-level semantics . they interpret successes through the geometry of the representation shifts based on isotropy . |
| Outcome: | The proposed model improves on many sentence-level NLP tasks, but it is not well understood why it works. |
Injecting Wiktionary to improve token-level contextual representations using contrastive learning (2024.eacl-short)
Copied to clipboard
| Challenge: | lexical semantics tasks require contextual word embeddings that are not blind to context, despite the fact that vectors of the same meaning are too different. |
| Approach: | They propose to fine-tune pre-trained language models by using automatically self-augmented examples to target contextual word embeddings. |
| Outcome: | The proposed method achieves significant improvements on the original WiC test set and in two new tests. |
Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 Loss (2024.emnlp-main)
Copied to clipboard
| Challenge: | despite its efficiency, Sentence-BERT ignores the progressive nature of semantic relationships, despite a promising approach . contrastive learning methods have improved performance on renowned STS benchmarks, but they fail to leverage fine-grained information. |
| Approach: | They propose a regression framework that categorizes text pairs as either semantically similar or dissimilar . they propose two loss functions: Translated ReLU and Smooth K2 Loss to bridge this gap . |
| Outcome: | The proposed method achieves convincing performance across seven established STS benchmarks. |
Achieving Stronger Generation via Simple Contrastive Tuning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent years have witnessed remarkable progress in large language models (LLMs). |
| Approach: | They propose a framework for contrastive decoding to enhance instruction-tuned models. |
| Outcome: | The proposed framework improves model performance without additional data or computational resources. |
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. |
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent advances in machine learning have led to the use of contrastive loss for representation learning. |
| Approach: | They propose to use batch-softmax contrastive loss to train pairwise sentence embeddings . they propose to take a batch-softermax contrastitive loss and train it with different loss functions . |
| Outcome: | The proposed model improves on a number of datasets and pairwise sentence scoring tasks. |
Developing Prefix-Tuning Models for Hierarchical Text Classification (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Hierarchical text classification (HTC) is a key task in many industrial applications. Pre-trained Language Models (PLMs) have become dominant for most natural language processing (NLP) tasks. |
| Approach: | They investigate how prefix tuning can improve hierarchical text classification . prefix-tuning model only needs less than 1% of parameters to achieve performance . |
| Outcome: | The proposed model can achieve comparable performance to regular full fine-tuning. |