@inproceedings{cerniavski-stymne-2022-uppsala,
title = "{U}ppsala {U}niversity at {S}em{E}val-2022 Task 1: Can Foreign Entries Enhance an {E}nglish Reverse Dictionary?",
author = "Cerniavski, Rafal and
Stymne, Sara",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.10",
doi = "10.18653/v1/2022.semeval-1.10",
pages = "88--93",
abstract = "We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set.",
}
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<abstract>We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set.</abstract>
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%0 Conference Proceedings
%T Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?
%A Cerniavski, Rafal
%A Stymne, Sara
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F cerniavski-stymne-2022-uppsala
%X We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set.
%R 10.18653/v1/2022.semeval-1.10
%U https://aclanthology.org/2022.semeval-1.10
%U https://doi.org/10.18653/v1/2022.semeval-1.10
%P 88-93
Markdown (Informal)
[Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?](https://aclanthology.org/2022.semeval-1.10) (Cerniavski & Stymne, SemEval 2022)
ACL