Proyag Pal and Kenneth Heafield.
To be published at NAACL 2022.
View 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.
Proyag Pal, Alham Fikri Aji, Pinzhen Chen, and Sukanta Sen.
Published at WMT21 at EMNLP 2021.
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View abstract We describe the University of Edinburgh’s Bengali↔Hindi constrained systems submitted to the WMT21 News Translation task. We submitted ensembles of Transformer models built with large-scale back-translation and fine-tuned on subsets of training data retrieved based on similarity to the target domain. For both translation directions, our submissions are among the best-performing constrained systems according to human evaluation.