@inproceedings{mickus-etal-2022-semeval,
title = "{S}emeval-2022 Task 1: {CODWOE} {--} Comparing Dictionaries and Word Embeddings",
author = "Mickus, Timothee and
Van Deemter, Kees and
Constant, Mathieu and
Paperno, Denis",
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-1.1",
doi = "10.18653/v1/2022.semeval-1.1",
pages = "1--14",
abstract = "Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.",
}
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%0 Conference Proceedings
%T Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings
%A Mickus, Timothee
%A Van Deemter, Kees
%A Constant, Mathieu
%A Paperno, Denis
%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 mickus-etal-2022-semeval
%X Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.
%R 10.18653/v1/2022.semeval-1.1
%U https://aclanthology.org/2022.semeval-1.1
%U https://doi.org/10.18653/v1/2022.semeval-1.1
%P 1-14
Markdown (Informal)
[Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings](https://aclanthology.org/2022.semeval-1.1) (Mickus et al., SemEval 2022)
ACL