Supervised Seeded Iterated Learning for Interactive Language Learning

Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville


Abstract
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 (SSIL) to combine both methods to minimize their respective weaknesses. We then show the effectiveness of in the language-drift translation game.
Anthology ID:
2020.emnlp-main.325
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3962–3970
Language:
URL:
https://aclanthology.org/2020.emnlp-main.325
DOI:
10.18653/v1/2020.emnlp-main.325
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.325.pdf
Video:
 https://slideslive.com/38939148
Data
Multi30k