Supervised Seeded Iterated Learning for Interactive Language Learning
This work addresses language drift for interactive language learning, but it is incremental as it builds on existing methods.
The paper tackles language drift in conversational agents by combining Supervised Selfplay and Seeded Iterated Learning to address their individual weaknesses, resulting in improved performance in a language-drift translation game.
Language drift has been one of the major obstacles to train language models through interaction. When word-based conversational agents are trained towards completing a task, they tend to invent their language rather than leveraging natural language. In recent literature, two general methods partially counter this phenomenon: Supervised Selfplay (S2P) and Seeded Iterated Learning (SIL). While S2P jointly trains interactive and supervised losses to counter the drift, SIL changes the training dynamics to prevent language drift from occurring. In this paper, we first highlight their respective weaknesses, i.e., late-stage training collapses and higher negative likelihood when evaluated on human corpus. Given these observations, we introduce Supervised Seeded Iterated Learning to combine both methods to minimize their respective weaknesses. We then show the effectiveness of \algo in the language-drift translation game.