@inproceedings{bendahman-etal-2022-bl,
title = "{BL}.{R}esearch at {S}em{E}val-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and {LSTM} autoencoders",
author = "Bendahman, Nihed and
Breton, Julien and
Nicolaieff, Lina and
Billami, Mokhtar Boumedyen and
Bortolaso, Christophe and
Miloudi, Youssef",
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-1.11",
doi = "10.18653/v1/2022.semeval-1.11",
pages = "94--100",
abstract = "This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 {``}CODWOE: Comparing Dictionaries and WOrd Embeddings{''}. We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks. All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears. We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings.",
}
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<abstract>This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks. All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears. We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings.</abstract>
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%0 Conference Proceedings
%T BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders
%A Bendahman, Nihed
%A Breton, Julien
%A Nicolaieff, Lina
%A Billami, Mokhtar Boumedyen
%A Bortolaso, Christophe
%A Miloudi, Youssef
%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 bendahman-etal-2022-bl
%X This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks. All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears. We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings.
%R 10.18653/v1/2022.semeval-1.11
%U https://aclanthology.org/2022.semeval-1.11
%U https://doi.org/10.18653/v1/2022.semeval-1.11
%P 94-100
Markdown (Informal)
[BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders](https://aclanthology.org/2022.semeval-1.11) (Bendahman et al., SemEval 2022)
ACL