Papers with neural agents
On the Relationship between Zipf’s Law of Abbreviation and Interfering Noise in Emergent Languages (2021.acl-srw)
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| Challenge: | Existing studies have shown that emergent languages do not obey ZLA when neural agents play a signaling game. |
| Approach: | They propose to add an explicit penalty on word lengths to a signaling game to simulate a ZLA-like tendency when interfering noises are added to the agents' environment. |
| Outcome: | The proposed model shows that the noise on a speaker is one of the factors for ZLA, while noise on the listener and a channel is not. |
The Emergence of High-Level Semantics in a Signaling Game (2024.starsem-1)
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| Challenge: | a symbol grounding problem has been raised in recent years in AI . we show that neural agents can communicate high-level semantic concepts . |
| Approach: | They propose to use an adversarial agent to train neural agents in a signaling game . they show that the agents can communicate high-level semantic concepts rather than low-level features . |
| Outcome: | The proposed method can learn to communicate high-level semantic concepts . it also produces an appropriate training signal when no other method is available . |
Co-evolution of language and agents in referential games (2021.eacl-main)
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| Challenge: | Referential games allow neural agents to learn language, but they do not take into account the learning biases of the learners. |
| Approach: | They propose to model cultural and architectural evolution in a population of agents to take into account learning biases of the language learners and let them co-evolve. |
| Outcome: | The proposed model outperforms cultural transmission in a population of agents and takes into account learning biases of the learners. |
On the Spontaneous Emergence of Discrete and Compositional Signals (2020.acl-main)
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| Challenge: | Using a continuous latent space, we are able to train using backpropagation and show discrete messages nevertheless naturally emerge. |
| Approach: | They propose a general framework to study language emergence through signaling games with neural agents. |
| Outcome: | The proposed framework shows that discrete messages naturally emerge and that they are not compositional. |
So many design choices: Improving and interpreting neural agent communication in signaling games (2023.findings-acl)
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| Challenge: | Emergent language games are experimental protocols designed to model how communication may arise among a group of agents. |
| Approach: | They propose to adopt a signaling game in which a sender is exposed to an image and generates a sequence of symbols that is transmitted to a receiver. |
| Outcome: | The proposed language improves when the sender is exposed to an image and generates a sequence of symbols that is transmitted to a receiver. |
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use (2025.findings-emnlp)
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| Challenge: | Lexical semantic change has been investigated with observational and experimental methods, but observational methods cannot get at causal mechanisms. |
| Approach: | They introduce a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system and then manipulating their communicative needs. |
| Outcome: | The proposed framework simulates the evolution of a lexical system within a single generation by grounding agents in a real lexicon and manipulating their communicative needs. |
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning (2021.emnlp-main)
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| Challenge: | Existing studies have focused on agent-based simulations of language emergence. |
| Approach: | They propose to model the trade-off between word order and inflection in natural languages by using neural network agents. |
| Outcome: | The results show that neural agents strive to maintain the utterance type distribution observed during learning, rather than developing a more efficient or systematic language. |