@inproceedings{lupo-etal-2023-encoding,
title = "Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation",
author = "Lupo, Lorenzo and
Dinarelli, Marco and
Besacier, Laurent",
editor = "Tafreshi, Shabnam and
Akula, Arjun and
Sedoc, Jo{\~a}o and
Drozd, Aleksandr and
Rogers, Anna and
Rumshisky, Anna",
booktitle = "Proceedings of the Fourth Workshop on Insights from Negative Results in NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.insights-1.4",
doi = "10.18653/v1/2023.insights-1.4",
pages = "33--44",
abstract = "Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture. This paper investigates the intuitive idea of providing the model with explicit information about the position of the sentences contained in the concatenation window. We compare various methods to encode sentence positions into token representations, including novel methods. Our results show that the Transformer benefits from certain sentence position encoding methods on English to Russian translation, if trained with a context-discounted loss. However, the same benefits are not observed on English to German. Further empirical efforts are necessary to define the conditions under which the proposed approach is beneficial.",
}
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%0 Conference Proceedings
%T Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation
%A Lupo, Lorenzo
%A Dinarelli, Marco
%A Besacier, Laurent
%Y Tafreshi, Shabnam
%Y Akula, Arjun
%Y Sedoc, João
%Y Drozd, Aleksandr
%Y Rogers, Anna
%Y Rumshisky, Anna
%S Proceedings of the Fourth Workshop on Insights from Negative Results in NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F lupo-etal-2023-encoding
%X Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture. This paper investigates the intuitive idea of providing the model with explicit information about the position of the sentences contained in the concatenation window. We compare various methods to encode sentence positions into token representations, including novel methods. Our results show that the Transformer benefits from certain sentence position encoding methods on English to Russian translation, if trained with a context-discounted loss. However, the same benefits are not observed on English to German. Further empirical efforts are necessary to define the conditions under which the proposed approach is beneficial.
%R 10.18653/v1/2023.insights-1.4
%U https://aclanthology.org/2023.insights-1.4
%U https://doi.org/10.18653/v1/2023.insights-1.4
%P 33-44
Markdown (Informal)
[Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation](https://aclanthology.org/2023.insights-1.4) (Lupo et al., insights-WS 2023)
ACL