Summary of Multimuc: Multilingual Template Filling on Muc-4, by William Gantt et al.
MultiMUC: Multilingual Template Filling on MUC-4
by William Gantt, Shabnam Behzad, Hannah YoungEun An, Yunmo Chen, Aaron Steven White, Benjamin Van Durme, Mahsa Yarmohammadi
First submitted to arxiv on: 29 Jan 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 In this paper, researchers introduce a multilingual parallel corpus called MultiMUC, designed specifically for template filling tasks. The corpus consists of translations of the classic MUC-4 benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian. The translations were obtained through automatic machine translation systems, while manual projections of original English annotations were applied to each target language. Additionally, human translations were provided for dev and test splits containing annotated template arguments in all languages. To evaluate the effectiveness of MultiMUC, baselines are presented using state-of-the-art template filling models as well as ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool called MultiMUC that helps computers understand how to fill in templates with missing information. The tool is important because it can help computers understand instructions and tasks written in different languages. To make this happen, the researchers translated an old template filling test into five new languages: Arabic, Chinese, Farsi, Korean, and Russian. They also added human translations to some parts of the test so that computers could learn from them. |
Keywords
» Artificial intelligence » Translation
