@inproceedings{gillin-2022-encoder,
title = "Is Encoder-Decoder Transformer the Shiny Hammer?",
author = "Gillin, Nat",
editor = {Scherrer, Yves and
Jauhiainen, Tommi and
Ljube{\v{s}}i{\'c}, Nikola and
Nakov, Preslav and
Tiedemann, J{\"o}rg and
Zampieri, Marcos},
booktitle = "Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.vardial-1.9",
pages = "80--85",
abstract = "We present an approach to multi-class classification using an encoder-decoder transformer model. We trained a network to identify French varieties using the same scripts we use to train an encoder-decoder machine translation model. With some slight modification to the data preparation and inference parameters, we showed that the same tools used for machine translation can be easily re-used to achieve competitive performance for classification. On the French Dialectal Identification (FDI) task, we scored 32.4 on weighted F1, but this is far from a simple naive bayes classifier that outperforms a neural encoder-decoder model at 41.27 weighted F1.",
}
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%0 Conference Proceedings
%T Is Encoder-Decoder Transformer the Shiny Hammer?
%A Gillin, Nat
%Y Scherrer, Yves
%Y Jauhiainen, Tommi
%Y Ljubešić, Nikola
%Y Nakov, Preslav
%Y Tiedemann, Jörg
%Y Zampieri, Marcos
%S Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F gillin-2022-encoder
%X We present an approach to multi-class classification using an encoder-decoder transformer model. We trained a network to identify French varieties using the same scripts we use to train an encoder-decoder machine translation model. With some slight modification to the data preparation and inference parameters, we showed that the same tools used for machine translation can be easily re-used to achieve competitive performance for classification. On the French Dialectal Identification (FDI) task, we scored 32.4 on weighted F1, but this is far from a simple naive bayes classifier that outperforms a neural encoder-decoder model at 41.27 weighted F1.
%U https://aclanthology.org/2022.vardial-1.9
%P 80-85
Markdown (Informal)
[Is Encoder-Decoder Transformer the Shiny Hammer?](https://aclanthology.org/2022.vardial-1.9) (Gillin, VarDial 2022)
ACL
- Nat Gillin. 2022. Is Encoder-Decoder Transformer the Shiny Hammer?. In Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 80–85, Gyeongju, Republic of Korea. Association for Computational Linguistics.