@inproceedings{lyu-etal-2022-mllabs,
title = "{MLL}abs-{LIG} at {T}empo{W}i{C} 2022: A Generative Approach for Examining Temporal Meaning Shift",
author = "Lyu, Chenyang and
Zhou, Yongxin and
Ji, Tianbo",
editor = "Barbieri, Francesco and
Camacho-Collados, Jose and
Dhingra, Bhuwan and
Espinosa-Anke, Luis and
Gribovskaya, Elena and
Lazaridou, Angeliki and
Loureiro, Daniel and
Neves, Leonardo",
booktitle = "Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.evonlp-1.1",
doi = "10.18653/v1/2022.evonlp-1.1",
pages = "1--6",
abstract = "In this paper, we present our system for the EvoNLP 2022 shared task Temporal Meaning Shift (TempoWiC). Different from the typically used discriminative model, we propose a generative approach based on pre-trained generation models. The basic architecture of our system is a seq2seq model where the input sequence consists of two documents followed by a question asking whether the meaning of target word changed or not, the target output sequence is a declarative sentence describing the meaning of target word changed or not. The experimental results on TempoWiC test set show that our best system (with time information) obtained an accuracy and Marco F-1 score of 68.09{\%} and 62.59{\%} respectively, which ranked 12th among all submitted systems. The results have shown the plausibility of using generation model for WiC tasks, meanwhile also indicate there{'}s still room for further improvement.",
}
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%0 Conference Proceedings
%T MLLabs-LIG at TempoWiC 2022: A Generative Approach for Examining Temporal Meaning Shift
%A Lyu, Chenyang
%A Zhou, Yongxin
%A Ji, Tianbo
%Y Barbieri, Francesco
%Y Camacho-Collados, Jose
%Y Dhingra, Bhuwan
%Y Espinosa-Anke, Luis
%Y Gribovskaya, Elena
%Y Lazaridou, Angeliki
%Y Loureiro, Daniel
%Y Neves, Leonardo
%S Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F lyu-etal-2022-mllabs
%X In this paper, we present our system for the EvoNLP 2022 shared task Temporal Meaning Shift (TempoWiC). Different from the typically used discriminative model, we propose a generative approach based on pre-trained generation models. The basic architecture of our system is a seq2seq model where the input sequence consists of two documents followed by a question asking whether the meaning of target word changed or not, the target output sequence is a declarative sentence describing the meaning of target word changed or not. The experimental results on TempoWiC test set show that our best system (with time information) obtained an accuracy and Marco F-1 score of 68.09% and 62.59% respectively, which ranked 12th among all submitted systems. The results have shown the plausibility of using generation model for WiC tasks, meanwhile also indicate there’s still room for further improvement.
%R 10.18653/v1/2022.evonlp-1.1
%U https://aclanthology.org/2022.evonlp-1.1
%U https://doi.org/10.18653/v1/2022.evonlp-1.1
%P 1-6
Markdown (Informal)
[MLLabs-LIG at TempoWiC 2022: A Generative Approach for Examining Temporal Meaning Shift](https://aclanthology.org/2022.evonlp-1.1) (Lyu et al., EvoNLP 2022)
ACL