Summary of Evaluation Of Machine Translation Based on Semantic Dependencies and Keywords, by Kewei Yuan and Qiurong Zhao and Yang Xu and Xiao Zhang and Huansheng Ning
Evaluation of Machine Translation Based on Semantic Dependencies and Keywords
by Kewei Yuan, Qiurong Zhao, Yang Xu, Xiao Zhang, Huansheng Ning
First submitted to arxiv on: 20 Apr 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 The paper proposes a computational method for evaluating the semantic correctness of machine translations by incorporating semantic dependencies and sentence keyword information. The method uses a language technology platform to analyze sentences and extract semantic dependency graphs, keywords, and weight information corresponding to keywords. This approach includes all word information with semantic dependencies in the sentence and keyword information that affects semantic information. The authors construct semantic association pairs including word and dependency multi-features. The experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve machine translation by evaluating its semantic correctness. Right now, most evaluation algorithms just look at words and sentences without considering their deeper meaning. This new method looks at how words relate to each other in a sentence and which keywords are important for understanding the sentence’s meaning. The result is a more accurate way to evaluate machine translations. |
Keywords
» Artificial intelligence » Translation
