Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off (2023.tacl-1)
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| Challenge: | Existing models of language learning with neural agents lack appropriate cognitive biases in artificial learners. |
| Approach: | They propose a framework where speaking and listening agents learn a miniature language via supervised learning and optimize it for communication via reinforcement learning. |
| Outcome: | The proposed framework replicates the word-order/case-marking trade-off without hard-coding biases in the agents. |
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