Towards Multi-Agent Communication-Based Language Learning
Angeliki Lazaridou Nghia The Pham Marco Baroni University of Trento
Abstract
We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an ad-hoc communication code only effective for the game they are playing.
原文 arXiv:1605.07133;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1605.07133v1