@inproceedings{lee-etal-2019-countering,
title = "Countering Language Drift via Visual Grounding",
author = "Lee, Jason and
Cho, Kyunghyun and
Kiela, Douwe",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-1447/",
doi = "10.18653/v1/D19-1447",
pages = "4385--4395",
abstract = "Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a non-linguistic reward is used in a goal-based task, e.g. some scalar success metric, the communication protocol may easily and radically diverge from natural language. We recast translation as a multi-agent communication game and examine auxiliary training constraints for their effectiveness in mitigating language drift. We show that a combination of syntactic (language model likelihood) and semantic (visual grounding) constraints gives the best communication performance, allowing pre-trained agents to retain English syntax while learning to accurately convey the intended meaning."
}
Markdown (Informal)
[Countering Language Drift via Visual Grounding](https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-1447/) (Lee et al., EMNLP-IJCNLP 2019)
ACL
- Jason Lee, Kyunghyun Cho, and Douwe Kiela. 2019. Countering Language Drift via Visual Grounding. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4385–4395, Hong Kong, China. Association for Computational Linguistics.