Summary of Learning Translations: Emergent Communication Pretraining For Cooperative Language Acquisition, by Dylan Cope and Peter Mcburney
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition
by Dylan Cope, Peter McBurney
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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 presents a novel AI challenge called Cooperative Language Acquisition Problem (CLAP) that relaxes Zero-Shot Coordination (ZSC) assumptions by allowing a ‘joiner’ agent to learn from interactions between agents in a target community. The goal is to develop communication strategies that are robust across different communities. Two methods are proposed and compared: Imitation Learning (IL), which learns from the data, and Emergent Communication pretraining and Translation Learning (ECTL), which combines self-play with EC and translation learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers propose a new AI challenge called Cooperative Language Acquisition Problem (CLAP) that helps agents learn to communicate with each other even if they haven’t seen each other before. They want to find ways for agents to share information that work across different groups of agents. To do this, they suggest two approaches: one that learns from data and another that combines learning a new language with translating between languages. |
Keywords
* Artificial intelligence * Pretraining * Translation * Zero shot
