Summary of Concept-best-matching: Evaluating Compositionality in Emergent Communication, by Boaz Carmeli et al.
Concept-Best-Matching: Evaluating Compositionality in Emergent Communication
by Boaz Carmeli, Yonatan Belinkov, Ron Meir
First submitted to arxiv on: 17 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 The proposed procedure assesses the compositionality of emergent communication by finding the best-match between emerged words and natural language concepts. This evaluation method provides a global score and a translation-map from emergent words to natural language concepts, offering a direct and interpretable mapping between emergent words and human concepts for the first time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial agents learn to communicate to accomplish tasks, but their protocols are often unclear to humans. Many studies have tried to evaluate this emergent communication using different measures, with one key goal being “compositionality”. However, current methods don’t directly show how well this compositionality works. To solve this, the proposed procedure finds the best match between words and natural language concepts. This gives us a score and a map showing how emerged words relate to human ideas. |
Keywords
» Artificial intelligence » Translation
