@inproceedings{yang-etal-2023-upon,
title = "Once Upon a ${\it Time}$ in ${\it Graph}$: Relative-Time Pretraining for Complex Temporal Reasoning",
author = "Yang, Sen and
Li, Xin and
Bing, Lidong and
Lam, Wai",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.728",
doi = "10.18653/v1/2023.emnlp-main.728",
pages = "11879--11895",
abstract = "Our physical world is constantly evolving over time, rendering challenges for pre-trained language models to understand and reason over the temporal contexts of texts. Existing work focuses on strengthening the direct association between a piece of text and its time-stamp. However, the knowledge-time association is usually insufficient for the downstream tasks that require reasoning over temporal dependencies between knowledge. In this work, we make use of the underlying nature of time, all temporally-scoped sentences are strung together through a one-dimensional time axis, and suggest creating a graph structure based on the relative placements of events along the time axis. Inspired by the graph view, we propose RemeMo ($\underline{Re}lative Ti\underline{me} \underline{Mo}deling$), which explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences. Experimental results show that RemeMo outperforms the baseline T5 on multiple temporal question answering datasets under various settings. Further analysis suggests that RemeMo is especially good at modeling long-range complex temporal dependencies.",
}
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<abstract>Our physical world is constantly evolving over time, rendering challenges for pre-trained language models to understand and reason over the temporal contexts of texts. Existing work focuses on strengthening the direct association between a piece of text and its time-stamp. However, the knowledge-time association is usually insufficient for the downstream tasks that require reasoning over temporal dependencies between knowledge. In this work, we make use of the underlying nature of time, all temporally-scoped sentences are strung together through a one-dimensional time axis, and suggest creating a graph structure based on the relative placements of events along the time axis. Inspired by the graph view, we propose RemeMo (\underlineRelative Ti\underlineme \underlineModeling), which explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences. Experimental results show that RemeMo outperforms the baseline T5 on multiple temporal question answering datasets under various settings. Further analysis suggests that RemeMo is especially good at modeling long-range complex temporal dependencies.</abstract>
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%0 Conference Proceedings
%T Once Upon a Time in Graph: Relative-Time Pretraining for Complex Temporal Reasoning
%A Yang, Sen
%A Li, Xin
%A Bing, Lidong
%A Lam, Wai
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-upon
%X Our physical world is constantly evolving over time, rendering challenges for pre-trained language models to understand and reason over the temporal contexts of texts. Existing work focuses on strengthening the direct association between a piece of text and its time-stamp. However, the knowledge-time association is usually insufficient for the downstream tasks that require reasoning over temporal dependencies between knowledge. In this work, we make use of the underlying nature of time, all temporally-scoped sentences are strung together through a one-dimensional time axis, and suggest creating a graph structure based on the relative placements of events along the time axis. Inspired by the graph view, we propose RemeMo (\underlineRelative Ti\underlineme \underlineModeling), which explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences. Experimental results show that RemeMo outperforms the baseline T5 on multiple temporal question answering datasets under various settings. Further analysis suggests that RemeMo is especially good at modeling long-range complex temporal dependencies.
%R 10.18653/v1/2023.emnlp-main.728
%U https://aclanthology.org/2023.emnlp-main.728
%U https://doi.org/10.18653/v1/2023.emnlp-main.728
%P 11879-11895
Markdown (Informal)
[Once Upon a Time in Graph: Relative-Time Pretraining for Complex Temporal Reasoning](https://aclanthology.org/2023.emnlp-main.728) (Yang et al., EMNLP 2023)
ACL