Lessons Learned in Multilingual Grounded Language Learning

Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté, Grzegorz Chrupała, Afra Alishahi


Abstract
Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.
Anthology ID:
K18-1039
Volume:
Proceedings of the 22nd Conference on Computational Natural Language Learning
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
402–412
Language:
URL:
https://aclanthology.org/K18-1039
DOI:
10.18653/v1/K18-1039
Bibkey:
Cite (ACL):
Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté, Grzegorz Chrupała, and Afra Alishahi. 2018. Lessons Learned in Multilingual Grounded Language Learning. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 402–412, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Lessons Learned in Multilingual Grounded Language Learning (Kádár et al., CoNLL 2018)
Copy Citation:
PDF:
https://aclanthology.org/K18-1039.pdf
Code
 kadarakos/mulisera