@inproceedings{rosendahl-etal-2019-analysis,
title = "Analysis of Positional Encodings for Neural Machine Translation",
author = "Rosendahl, Jan and
Tran, Viet Anh Khoa and
Wang, Weiyue and
Ney, Hermann",
editor = {Niehues, Jan and
Cattoni, Rolando and
St{\"u}ker, Sebastian and
Negri, Matteo and
Turchi, Marco and
Ha, Thanh-Le and
Salesky, Elizabeth and
Sanabria, Ramon and
Barrault, Loic and
Specia, Lucia and
Federico, Marcello},
booktitle = "Proceedings of the 16th International Conference on Spoken Language Translation",
month = nov # " 2-3",
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2019.iwslt-1.20",
abstract = "In this work we analyze and compare the behavior of the Transformer architecture when using different positional encoding methods. While absolute and relative positional encoding perform equally strong overall, we show that relative positional encoding is vastly superior (4.4{\%} to 11.9{\%} BLEU) when translating a sentence that is longer than any observed training sentence. We further propose and analyze variations of relative positional encoding and observe that the number of trainable parameters can be reduced without a performance loss, by using fixed encoding vectors or by removing some of the positional encoding vectors.",
}
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<abstract>In this work we analyze and compare the behavior of the Transformer architecture when using different positional encoding methods. While absolute and relative positional encoding perform equally strong overall, we show that relative positional encoding is vastly superior (4.4% to 11.9% BLEU) when translating a sentence that is longer than any observed training sentence. We further propose and analyze variations of relative positional encoding and observe that the number of trainable parameters can be reduced without a performance loss, by using fixed encoding vectors or by removing some of the positional encoding vectors.</abstract>
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%0 Conference Proceedings
%T Analysis of Positional Encodings for Neural Machine Translation
%A Rosendahl, Jan
%A Tran, Viet Anh Khoa
%A Wang, Weiyue
%A Ney, Hermann
%Y Niehues, Jan
%Y Cattoni, Rolando
%Y Stüker, Sebastian
%Y Negri, Matteo
%Y Turchi, Marco
%Y Ha, Thanh-Le
%Y Salesky, Elizabeth
%Y Sanabria, Ramon
%Y Barrault, Loic
%Y Specia, Lucia
%Y Federico, Marcello
%S Proceedings of the 16th International Conference on Spoken Language Translation
%D 2019
%8 nov 2 3
%I Association for Computational Linguistics
%C Hong Kong
%F rosendahl-etal-2019-analysis
%X In this work we analyze and compare the behavior of the Transformer architecture when using different positional encoding methods. While absolute and relative positional encoding perform equally strong overall, we show that relative positional encoding is vastly superior (4.4% to 11.9% BLEU) when translating a sentence that is longer than any observed training sentence. We further propose and analyze variations of relative positional encoding and observe that the number of trainable parameters can be reduced without a performance loss, by using fixed encoding vectors or by removing some of the positional encoding vectors.
%U https://aclanthology.org/2019.iwslt-1.20
Markdown (Informal)
[Analysis of Positional Encodings for Neural Machine Translation](https://aclanthology.org/2019.iwslt-1.20) (Rosendahl et al., IWSLT 2019)
ACL