NAACL 2022long2 citations
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation
Abstract
This paper describes a method to quantify the amount of information H(t|s) added by the target sentence t that is not present in the source s in a neural machine translation system. We do this by providing the model the target sentence in a highly compressed form (a “cheat code”), and exploring the effect of the size of the cheat code. We find that the model is able to capture extra information from just a single float representation of the target and nearly reproduces the target with two 32-bit floats per target token.
BibTeX
@inproceedings{pal-heafield-2022-cheat,
title = "Cheat Codes to Quantify Missing Source Information in Neural Machine Translation",
author = "Pal, Proyag and
Heafield, Kenneth",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.177/",
doi = "10.18653/v1/2022.naacl-main.177",
pages = "2472--2477"
}