Towards Effective Time-Aware Language Representation

What is it about?

In this work, we introduce BiTimeBERT 2.0, a novel time-aware language model pre-trained on a temporal news article collection. BiTimeBERT 2.0 incorporates temporal information through three innovative pre-training objectives: Extended Time-Aware Masked Language Modeling (ETAMLM), Document Dating (DD), and Time-Sensitive Entity Replacement (TSER). Each objective is specifically designed to target a distinct dimension of temporal information: ETAMLM enhances the model's understanding of temporal contexts and relations, DD integrates document timestamps as explicit chronological markers, and TSER focuses on the temporal dynamics of "Person" entities. Moreover, our refined corpus preprocessing strategy reduces training time by nearly 53%, making BiTimeBERT 2.0 significantly more efficient while maintaining high performance. Experimental results show that BiTimeBERT 2.0 achieves substantial improvements across a broad range of time-related tasks and excels on datasets spanning extensive temporal ranges. These findings underscore BiTimeBERT 2.0's potential as a powerful tool for advancing temporal reasoning in NLP.

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Photo by Luca Bravo on Unsplash

Photo by Luca Bravo on Unsplash

Why is it important?

This work makes the following contributions: 1. We thoroughly explore the impact of integrating three distinct types of temporal information into pre-trained language models. Our findings reveal that infusing language models with temporal information improves their performance across various downstream time-related tasks, highlighting the importance of temporal context. 2. We introduce BiTimeBERT 2.0, a novel time-aware language model specifically trained through three pre-training objectives. In addition, we implement several strategic enhancements, including the refinement of time-aware masked language modeling, the introduction of time-sensitive entity replacement objectives, and more efficient training with a focused temporal news corpus, all of which contribute to more effective time-aware representations. 3. We perform extensive experiments across a range of time-related tasks, illustrating BiTimeBERT 2.0's superior ability to capture and leverage temporal information compared to existing models. Notably, BiTimeBERT 2.0 excels in generalizing to challenging datasets outside its pre-training temporal scope. These results not only validate our model's enhanced capacity for generating time-aware representations but also showcase its practical effectiveness for real-world applications.