Papers with representation
Contrastive Learning of Sociopragmatic Meaning in Social Media (2023.findings-acl)
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| Challenge: | Recent progress in representation and contrastive learning in NLP has not considered the class of sociopragmatic meaning (i.e., meaning in interaction within different language communities). |
| Approach: | They propose a framework for learning task-agnostic representations transferable to a wide range of sociopragmatic tasks. |
| Outcome: | The proposed framework outperforms other contrastive learning frameworks for both in-domain and out-of-domain data, across both the general and few-shot settings. |
Reinforced Training Data Selection for Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold. |
| Approach: | They propose a framework that searches for training instances relevant to the target domain and learns better representations for them. |
| Outcome: | The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks. |
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing (D18-1)
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| Challenge: | Accurate and complete knowledge bases (KBs) are paramount in NLP. |
| Approach: | They employ multiview learning for increasing the accuracy and coverage of entity type information in KBs by taking high- and low-resource languages from Wikipedia. |
| Outcome: | The proposed learning improves the accuracy and coverage of knowledge bases (KBs) by combining language and representation. |
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)
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| Challenge: | Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector. |
| Approach: | They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters . |
| Outcome: | The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations . |
An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025.coling-main)
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| Challenge: | Existing methods for tabular prediction rely on extensive pre-training or fine-tuning of LLMs . a retrieval-based approach eliminates the need for training any modules or performing data augmentation . |
| Approach: | They propose a retrieval-based approach that utilizes the powerful capabilities of large language models in representation, comprehension, and inference. |
| Outcome: | The proposed method exhibits strong predictive performance on tabular prediction task, affirming its practicality and effectiveness. |