Summary of Mixred: a Mix-lingual Relation Extraction Dataset, by Lingxing Kong et al.
MixRED: A Mix-lingual Relation Extraction Dataset
by Lingxing Kong, Yougang Chu, Zheng Ma, Jianbing Zhang, Liang He, Jiajun Chen
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 explores relation extraction in a mix-lingual scenario, where individuals combine language styles from different languages. The existing research focuses on monolingual or cross-lingual approaches, leaving a gap in understanding how to effectively extract relations in such scenarios. To address this issue, the authors introduce the task of MixRE (relation extraction) and create a human-annotated dataset called MixRED to support this task. They evaluate state-of-the-art supervised models and large language models on the MixRED dataset, highlighting their strengths and limitations. The study also investigates factors influencing model performance in the mix-lingual scenario and identifies promising directions for improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand relationships between things when people write in different languages within sentences. Right now, computers are great at understanding relationships in one language or another. But what happens when someone writes a sentence that mixes words from two or more languages? This is called code-switching. The authors of this paper create a special dataset and test existing computer programs on it to see how well they do. They find out which programs work best and why some don’t work as well. |
Keywords
» Artificial intelligence » Supervised
