Summary of Sememelm: a Sememe Knowledge Enhanced Method For Long-tail Relation Representation, by Shuyi Li and Shaojuan Wu and Xiaowang Zhang and Zhiyong Feng
SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation
by Shuyi Li, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel method for identifying relations between words without their contexts, particularly focusing on long-tail relations that are often uncommon in training data. The existing approaches based on language models (LMs) capture uncommon relations but overlook less frequent and meaningful ones. To address this issue, the authors introduce a sememe knowledge enhanced method (SememeLM), which utilizes external knowledge to enrich LMs. Sememes can break contextual constraints between words, allowing for more accurate representation of long-tail relations. The approach consists of graph encoding and consistency alignment modules to reduce noise and integrate external knowledge into LMs. Experimental results on word analogy datasets show that the proposed method outperforms some state-of-the-art methods in distinguishing subtle differences in relation representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding connections between words, which is important for many applications. The problem is that most approaches use language models to do this, but they don’t catch all the relationships because they’re based on common patterns found in training data. Long-tail relationships are especially tricky because there’s not enough information to learn from them. To solve this, researchers propose a new method called SememeLM, which uses external knowledge to help language models understand these long-tail relationships better. They create a graph and an alignment module to make sure the extra knowledge is useful and doesn’t get in the way. The results show that their approach works better than some other methods for recognizing subtle differences in relationships. |
Keywords
» Artificial intelligence » Alignment
