BERT got a Date: Introducing Transformers to Temporal Tagging

• URL: http://arxiv.org/abs/2109.14927v2
• Date: Mon, 4 Oct 2021 08:25:36 GMT
• Title: BERT got a Date: Introducing Transformers to Temporal Tagging
• Authors: Satya Almasian, Dennis Aumiller, Michael Gertz
• Abstract summary: We present a transformer encoder-decoder model using the RoBERTa language model as our best performing system. Our model surpasses previous works in temporal tagging and type classification, especially on rare classes.
• Score: 4.651578365545765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal expressions in text play a significant role in language understanding and correctly identifying them is fundamental to various retrieval and natural language processing systems. Previous works have slowly shifted from rule-based to neural architectures, capable of tagging expressions with higher accuracy. However, neural models can not yet distinguish between different expression types at the same level as their rule-based counterparts. In this work, we aim to identify the most suitable transformer architecture for joint temporal tagging and type classification, as well as, investigating the effect of semi-supervised training on the performance of these systems. Based on our study of token classification variants and encoder-decoder architectures, we present a transformer encoder-decoder model using the RoBERTa language model as our best performing system. By supplementing training resources with weakly labeled data from rule-based systems, our model surpasses previous works in temporal tagging and type classification, especially on rare classes. Our code and pre-trained experiments are available at: https://github.com/satya77/Transformer_Temporal_Tagger

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