{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bert-got-a-date-introducing-transformers-to","title":"BERT got a Date: Introducing Transformers to Temporal Tagging","arxiv_id":"2109.14927","date":"2021-09-30","proceeding":null,"authors":["Satya Almasian","Dennis Aumiller","Michael Gertz"],"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","url_abs":"https://arxiv.org/abs/2109.14927v3","url_pdf":"https://arxiv.org/pdf/2109.14927v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bert-got-a-date-introducing-transformers-to","repo_url":"https://github.com/satya77/Transformer_Temporal_Tagger","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"temporal-tagging","task_name":"Temporal Tagging"},{"task_slug":"token-classification","task_name":"Token Classification"},{"task_slug":"token-classification","task_name":"token-classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roberta","method_name":"RoBERTa"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-tagging-on-tempeval-3","task":"Temporal Tagging","dataset":"TempEval-3","model":"R2R","rank_in_archive_order":1,"of":5,"metrics":{" Strict Detection (Pr.)":"96.37"," Strict Detection (Re.)":"96.37","Relaxed Detection (F1)":"100","Relaxed Detection (Pr.)":"100","Relaxed Detection (Re.)":"100","Strict Detection (F1)":"96.37","Type":"90.43"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-tagging-on-tempeval-3","task":"Temporal Tagging","dataset":"TempEval-3","model":"B2B","rank_in_archive_order":2,"of":5,"metrics":{" Strict Detection (Pr.)":"94.11"," Strict Detection (Re.)":"81.01","Relaxed Detection (F1)":"92.52","Relaxed Detection (Pr.)":"100","Relaxed Detection (Re.)":"86.09","Strict Detection (F1)":"87.07","Type":"83.79"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-tagging-on-tempeval-3","task":"Temporal Tagging","dataset":"TempEval-3","model":"DateBERT","rank_in_archive_order":3,"of":5,"metrics":{" Strict Detection (Pr.)":"82.72"," Strict Detection (Re.)":"85.79","Relaxed Detection (F1)":"92.60","Relaxed Detection (Pr.)":"90.95","Relaxed Detection (Re.)":"94.35","Strict Detection (F1)":" 84.21","Type":"86.21"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-tagging-on-tempeval-3","task":"Temporal Tagging","dataset":"TempEval-3","model":"BERT-base","rank_in_archive_order":4,"of":5,"metrics":{" Strict Detection (Pr.)":"81.83"," Strict Detection (Re.)":"79.56","Relaxed Detection (F1)":"90.08","Relaxed Detection (Pr.)":"91.37","Relaxed Detection (Re.)":"88.84","Strict Detection (F1)":"80.67","Type":"82.00"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.14927","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}