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    <abstract>Temporal commonsense reasoning is a challenging task as it requires temporal knowledge usually not explicit in text. In this work, we propose an ensemble model for temporal commonsense reasoning. Our model relies on pre-trained contextual representations from transformer-based language models (i.e., BERT), and on a variety of training methods for enhancing model generalization: 1) multi-step fine-tuning using carefully selected auxiliary tasks and datasets, and 2) a specifically designed temporal masked language model task aimed to capture temporal commonsense knowledge. Our model greatly outperforms the standard fine-tuning approach and strong baselines on the MC-TACO dataset.</abstract>
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%0 Conference Proceedings
%T Towards a Language Model for Temporal Commonsense Reasoning
%A Kimura, Mayuko
%A Kanashiro Pereira, Lis
%A Kobayashi, Ichiro
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F kimura-etal-2021-towards-language
%X Temporal commonsense reasoning is a challenging task as it requires temporal knowledge usually not explicit in text. In this work, we propose an ensemble model for temporal commonsense reasoning. Our model relies on pre-trained contextual representations from transformer-based language models (i.e., BERT), and on a variety of training methods for enhancing model generalization: 1) multi-step fine-tuning using carefully selected auxiliary tasks and datasets, and 2) a specifically designed temporal masked language model task aimed to capture temporal commonsense knowledge. Our model greatly outperforms the standard fine-tuning approach and strong baselines on the MC-TACO dataset.
%U https://aclanthology.org/2021.ranlp-srw.12
%P 78-84

Markdown (Informal)

[Towards a Language Model for Temporal Commonsense Reasoning](https://aclanthology.org/2021.ranlp-srw.12) (Kimura et al., RANLP 2021)

• Towards a Language Model for Temporal Commonsense Reasoning (Kimura et al., RANLP 2021)

ACL

• Mayuko Kimura, Lis Kanashiro Pereira, and Ichiro Kobayashi. 2021. Towards a Language Model for Temporal Commonsense Reasoning. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 78–84, Online. INCOMA Ltd..