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    <abstract>This paper presents the transformer model built to participate in the SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes. It consists of an encoder-decoder architecture with multi-head attention mechanism. Its output is concatenated with the one hot encoding of the language label of an input character sequence to predict a target character sequence. The results show that the transformer outperforms the baseline rule-based system only partially.</abstract>
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%0 Conference Proceedings
%T A Transformer Architecture for the Prediction of Cognate Reflexes
%A Celano, Giuseppe G. A.
%Y Vylomova, Ekaterina
%Y Ponti, Edoardo
%Y Cotterell, Ryan
%S Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F celano-2022-transformer
%X This paper presents the transformer model built to participate in the SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes. It consists of an encoder-decoder architecture with multi-head attention mechanism. Its output is concatenated with the one hot encoding of the language label of an input character sequence to predict a target character sequence. The results show that the transformer outperforms the baseline rule-based system only partially.
%R 10.18653/v1/2022.sigtyp-1.10
%U https://aclanthology.org/2022.sigtyp-1.10/
%U https://doi.org/10.18653/v1/2022.sigtyp-1.10
%P 80-85

Markdown (Informal)

[A Transformer Architecture for the Prediction of Cognate Reflexes](https://aclanthology.org/2022.sigtyp-1.10/) (Celano, SIGTYP 2022)

• A Transformer Architecture for the Prediction of Cognate Reflexes (Celano, SIGTYP 2022)

ACL

• Giuseppe G. A. Celano. 2022. A Transformer Architecture for the Prediction of Cognate Reflexes. In Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 80–85, Seattle, Washington. Association for Computational Linguistics.