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- W4299352502 abstract "Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this work, we investigate methods for incorporating temporal information during pre-training to further improve the performance on time-related tasks. Compared with common pre-trained language models like BERT which utilize synchronic document collections (e.g., BookCorpus and Wikipedia) as the training corpora, we use long-span temporal news article collection for building word representations. We introduce BiTimeBERT, a novel language representation model trained on a temporal collection of news articles via two new pre-training tasks, which harnesses two distinct temporal signals to construct time-aware language representations. The experimental results show that BiTimeBERT consistently outperforms BERT and other existing pre-trained models with substantial gains on different downstream NLP tasks and applications for which time is of importance (e.g., the accuracy improvement over BERT is 155% on the event time estimation task)." @default.
- W4299352502 created "2022-10-02" @default.
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- W4299352502 date "2022-04-27" @default.
- W4299352502 modified "2023-09-27" @default.
- W4299352502 title "BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information" @default.
- W4299352502 doi "https://doi.org/10.48550/arxiv.2204.13032" @default.
- W4299352502 hasPublicationYear "2022" @default.
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