Summary Anaphoric Structure Emerges Between Neural Networks arxiv.org
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One Line
The article explores how anaphoric structure can develop in neural networks during communication training, without any imposed limitations.
Slides
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Key Points
- Anaphoric structure can emerge in artificial neural networks trained for communication tasks.
- Languages with anaphoric structures are learnable by neural models.
- Efficiency pressures on a speaker influence the emergence of anaphoric structure.
- Redundant roles in certain languages lead to increased ambiguity.
- Overt anaphoric forms in a language reduce ambiguity and aid understanding.
- The study shows that ambiguity is not a concern for the model's communicative accuracy.
- The emergence of anaphoric structure in neural networks has been explored in recent research.
- The study uses three different languages: No Elision, Pronoun, and Pro-drop.
Summaries
25 word summary
This article investigates the emergence of anaphoric structure in neural networks trained for communication. Anaphoric structures can be learned and emerge between models without constraints.
43 word summary
This article explores the emergence of anaphoric structure in artificial neural networks trained to solve a communicative task. Recent research has shown that languages with anaphoric structures can be learned by neural models, and these structures can emerge between models without additional constraints
287 word summary
This article explores the emergence of anaphoric structure in artificial neural networks trained to solve a communicative task. The study shows that languages with anaphoric structures are learnable by neural models, and these structures can emerge between models without additional constraints.
Recent research has explored the emergence of languages between neural agents trained to communicate. These languages are shaped by the biases of the networks and the objective for which they are trained. Previous work has focused on the conditions required for the emergence of syntactic structure and
Efficiency pressures on a speaker influence the emergence of anaphoric structure, which can arise from redundancy in communication. This highlights the role of the semantics-pragmatics interface in understanding the origins of anaphoric structure. The study uses a reconstruction
The text excerpt discusses an experiment on neural networks to determine if anaphoric structure can emerge between agents. The agents are trained using three different languages: No Elision, Pronoun, and Pro-drop. The Receiver is trained to map signals to meanings
Redundant roles in Pronoun and Pro-drop languages show increased ambiguity, while the No Elision language does not. Anaphoric structures lead to increased ambiguity and longer training time. Overt anaphoric forms in a language reduce ambiguity and aid
The study examines the emergence of anaphoric structure in neural networks. The authors find that all runs of their model achieve near-perfect communicative accuracy, indicating that ambiguity is not a concern for their results. They review three measures for evidence of an
This excerpt is a list of references and citations from various sources related to the emergence of language in neural networks. The sources cover topics such as processing complexity, communicative efficiency, semantic typology, linguistic communication in referential games, the theory of convers