@inproceedings{chen-zhao-2022-edinburgh,
title = "{E}dinburgh at {S}em{E}val-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions",
author = "Chen, Pinzhen and
Zhao, Zheng",
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-1.8",
doi = "10.18653/v1/2022.semeval-1.8",
pages = "75--81",
abstract = "This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks{'} best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading.",
}
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<title>Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions</title>
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<abstract>This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks’ best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading.</abstract>
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%0 Conference Proceedings
%T Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions
%A Chen, Pinzhen
%A Zhao, Zheng
%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 chen-zhao-2022-edinburgh
%X This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks’ best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading.
%R 10.18653/v1/2022.semeval-1.8
%U https://aclanthology.org/2022.semeval-1.8
%U https://doi.org/10.18653/v1/2022.semeval-1.8
%P 75-81
Markdown (Informal)
[Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions](https://aclanthology.org/2022.semeval-1.8) (Chen & Zhao, SemEval 2022)
ACL