تقرير
'LazImpa': Lazy and Impatient neural agents learn to communicate efficiently
العنوان: | 'LazImpa': Lazy and Impatient neural agents learn to communicate efficiently |
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المؤلفون: | Rita, Mathieu, Chaabouni, Rahma, Dupoux, Emmanuel |
المصدر: | Proceedings of CoNLL 2020 |
سنة النشر: | 2020 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, I.2 |
الوصف: | 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. Comment: Accepted to CoNLL 2020 |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2010.01878 |
رقم الأكسشن: | edsarx.2010.01878 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |