@inproceedings{tang-etal-2023-learning,
title = "Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation",
author = "Tang, Xiaohang and
Zhou, Yi and
Bollegala, Danushka",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.520",
doi = "10.18653/v1/2023.acl-long.520",
pages = "9352--9369",
abstract = "Dynamic contextualised word embeddings (DCWEs) represent the temporal semantic variations of words. We propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model (MLM) using time-sensitive templates. Given two snapshots $C_1$ and $C_2$ of a corpus taken respectively at two distinct timestamps $T_1$ and $T_2$, we first propose an unsupervised method to select (a) \textit{pivot} terms related to both $C_1$ and $C_2$, and (b) \textit{anchor} terms that are associated with a specific pivot term in each individual snapshot.We then generate prompts by filling manually compiled templates using the extracted pivot and anchor terms.Moreover, we propose an automatic method to learn time-sensitive templates from $C_1$ and $C_2$, without requiring any human supervision.Next, we use the generated prompts to adapt a pretrained MLM to $T_2$ by fine-tuning using those prompts.Multiple experiments show that our proposed method significantly reduces the perplexity of test sentences in $C_2$, outperforming the current state-of-the-art.",
}
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<abstract>Dynamic contextualised word embeddings (DCWEs) represent the temporal semantic variations of words. We propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model (MLM) using time-sensitive templates. Given two snapshots C₁ and C₂ of a corpus taken respectively at two distinct timestamps T₁ and T₂, we first propose an unsupervised method to select (a) pivot terms related to both C₁ and C₂, and (b) anchor terms that are associated with a specific pivot term in each individual snapshot.We then generate prompts by filling manually compiled templates using the extracted pivot and anchor terms.Moreover, we propose an automatic method to learn time-sensitive templates from C₁ and C₂, without requiring any human supervision.Next, we use the generated prompts to adapt a pretrained MLM to T₂ by fine-tuning using those prompts.Multiple experiments show that our proposed method significantly reduces the perplexity of test sentences in C₂, outperforming the current state-of-the-art.</abstract>
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%0 Conference Proceedings
%T Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation
%A Tang, Xiaohang
%A Zhou, Yi
%A Bollegala, Danushka
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F tang-etal-2023-learning
%X Dynamic contextualised word embeddings (DCWEs) represent the temporal semantic variations of words. We propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model (MLM) using time-sensitive templates. Given two snapshots C₁ and C₂ of a corpus taken respectively at two distinct timestamps T₁ and T₂, we first propose an unsupervised method to select (a) pivot terms related to both C₁ and C₂, and (b) anchor terms that are associated with a specific pivot term in each individual snapshot.We then generate prompts by filling manually compiled templates using the extracted pivot and anchor terms.Moreover, we propose an automatic method to learn time-sensitive templates from C₁ and C₂, without requiring any human supervision.Next, we use the generated prompts to adapt a pretrained MLM to T₂ by fine-tuning using those prompts.Multiple experiments show that our proposed method significantly reduces the perplexity of test sentences in C₂, outperforming the current state-of-the-art.
%R 10.18653/v1/2023.acl-long.520
%U https://aclanthology.org/2023.acl-long.520
%U https://doi.org/10.18653/v1/2023.acl-long.520
%P 9352-9369
Markdown (Informal)
[Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation](https://aclanthology.org/2023.acl-long.520) (Tang et al., ACL 2023)
ACL