Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji
| Challenge: | Pretrained audio-language models such as AudioCLIP and AudioCLAP have shown promising results on vision-language (VL) tasks. |
| Approach: | They extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance. |
| Outcome: | The proposed model improves on visual-language (VL) and audio-language tasks when the amount of training data is large. |
Similar Papers
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
Copied to clipboard
Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
| Approach: | They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text. |
| Outcome: | The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences. |
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to build a strong multilingual multimodal representation model are lacking in good-quality text-image pairs. |
| Approach: | They propose a method to build a strong multilingual multimodal representation model using English text-image pairs instead of a model from scratch. |
| Outcome: | The proposed model outperforms the original CLIP model on multilingual multimodal benchmarks. |
UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)
Copied to clipboard
| Challenge: | Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks . |
| Approach: | They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation. |
| Outcome: | The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search. |
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
Copied to clipboard
| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
| Outcome: | The proposed tasks capture key linguistic features of sentences, but they are difficult to infer from them. |
Learning Visually Grounded Sentence Representations (N18-1)
Copied to clipboard
| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
| Outcome: | The proposed model improves on word embeddings and word representations on standard benchmarks. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| 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. |
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing contrastive methods for learning universal sentence embeddings have limitations due to their over-parameterization and poor performance under domain shift settings. |
| Approach: | They propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power of contrastive learning for sentence embeddings by combining PLMs with energy-based learning. |
| Outcome: | Empirical results show that the proposed method improves on seven standard semantic textual similarity tasks and a domain-shifted STS task. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
Copied to clipboard
| 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. |
| Outcome: | The proposed methods produce document-level representations from sentences in 8 languages . the results show that a clever combination of sentence embeddings is usually better than encoding the full document as a single unit. |
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations (2021.acl-long)
Copied to clipboard
| 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. |
Positional Artefacts Propagate Through Masked Language Model Embeddings (2021.acl-long)
Copied to clipboard
| Challenge: | Existing word embedding models have a uniform pitfall in assigning a static vector to a word type. |
| Approach: | They propose a neuron-level analysis method to investigate the source of this information by comparing outlier neurons within BERT and RoBERTa’s hidden state vectors. |
| Outcome: | The proposed method pre-trains the RoBERTa-based models and shows that the outliers disappear without positional embeddings. |