Proyag Pal
2022
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation
Proyag Pal
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Kenneth Heafield
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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.
2021
The University of Edinburgh’s Bengali-Hindi Submissions to the WMT21 News Translation Task
Proyag Pal
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Alham Fikri Aji
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Pinzhen Chen
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Sukanta Sen
Proceedings of the Sixth Conference on Machine Translation
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.
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