Summary of Hncse: Advancing Sentence Embeddings Via Hybrid Contrastive Learning with Hard Negatives, by Wenxiao Liu et al.
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives
by Wenxiao Liu, Zihong Yang, Chaozhuo Li, Zijin Hong, Jianfeng Ma, Zhiquan Liu, Litian Zhang, Feiran Huang
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of unsupervised sentence representation learning in natural language processing (NLP). Recent success with contrastive learning techniques has led to the development of methods that prioritize optimization using negative samples. In computer vision, hard negative samples have been shown to enhance representation learning, but adapting this approach to text is complex due to its intricate syntactic and semantic details. The authors propose HNCSE, a novel framework that extends the leading SimCSE approach by incorporating hard negative samples to learn both positive and negative samples. This enhances the semantic understanding of sentences. Empirical tests on semantic textual similarity and transfer task datasets validate the superiority of HNCSE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand text better without needing labeled training data. It builds on a technique called contrastive learning, which has been successful in this area. The main idea is to use hard negative samples, which are sentences that are very similar but not exactly alike. This helps the computer learn more about what makes two sentences different or similar. The authors propose a new method called HNCSE, which uses hard negative samples to improve sentence representation learning. They tested it on several datasets and showed that it outperformed other methods. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Optimization » Representation learning » Unsupervised
