Abstract
Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, “LazImpa”, where the speaker is made increasingly lazy, i.e., avoids long messages, and the listener impatient, i.e., seeks to guess the intended content as soon as possible.- Anthology ID:
- 2020.conll-1.26
- Volume:
- Proceedings of the 24th Conference on Computational Natural Language Learning
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Raquel Fernández, Tal Linzen
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 335–343
- Language:
- URL:
- https://aclanthology.org/2020.conll-1.26
- DOI:
- 10.18653/v1/2020.conll-1.26
- Cite (ACL):
- Mathieu Rita, Rahma Chaabouni, and Emmanuel Dupoux. 2020. “LazImpa”: Lazy and Impatient neural agents learn to communicate efficiently. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 335–343, Online. Association for Computational Linguistics.
- Cite (Informal):
- “LazImpa”: Lazy and Impatient neural agents learn to communicate efficiently (Rita et al., CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.conll-1.26.pdf