| Challenge: | a recent study examines how document classification models trained during one time period perform on documents trained during other time periods. |
| Approach: | They propose to use a domain adaptation approach to adjust for changes in time to improve document classification. |
| Outcome: | The proposed model improves on documents trained on time intervals even on future time interval intervals. |
Similar Papers
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021.findings-emnlp)
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| Challenge: | Language use differs between domains and even within a domain, language use changes over time. |
| Approach: | They propose to use social media comments to study temporal adaptations in pre-trained language models. |
| Outcome: | The proposed model performs better on past than on future test sets, whereas adapting to domain does not improve performance on the downstream task. |
Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Experts (2025.naacl-long)
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| Challenge: | Existing classification models only consider the temporal variations of existing data . current models focus on English corpora, leaving time as domains unexplored . |
| Approach: | They propose a framework to generalize classifiers over time on four languages, English, Danish, French, and German. |
| Outcome: | The proposed framework can generalize classifiers over time on four languages, English, Danish, French, and German. |
Temporal Effects on Pre-trained Models for Language Processing Tasks (2022.tacl-1)
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| Challenge: | a recent study shows that language models can be improved as time passes . a number of approaches to solving language tasks have evolved rapidly without a model . |
| Approach: | They examine temporal effects on model performance on downstream language tasks . they also examine the efficacy of two approaches for temporal domain adaptation without human annotations . |
| Outcome: | The proposed methods improve self-labeling and named entity recognition on new data. |
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2022.naacl-main)
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| Challenge: | a pretrained model is optionally adapted through domain-specific pretraining, followed by task-specific finetuning. |
| Approach: | They establish a suite of eight tasks across different domains to quantify the effects of temporal misalignment in modern NLP systems. |
| Outcome: | The proposed tasks are based on eight domains and periods of time spanning five years or more and show that they have stronger effects than previous studies. |
Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025.naacl-long)
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
| Approach: | They introduce a new evaluation framework that employs fresh text and event prediction for assessing LLMs’ temporal adaptability. |
| Outcome: | The proposed framework shows significant temporal biases and a decline in performance over time. |
Diachronic word embeddings and semantic shifts: a survey (C18-1)
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| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
| Outcome: | The proposed methods are compared with existing methods and outline their main challenges and potential applications. |
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting (2022.findings-acl)
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| Challenge: | Large-scale multi-label document classification presents interesting challenges due to the large label space and two-tiered skewed label distributions. |
| Approach: | They evaluate several group-robust optimization algorithms proposed to mitigate temporal concept drift and class imbalance in document classification. |
| Outcome: | The proposed algorithms outperform sampling-based approaches to class imbalance and concept drift and lead to much better performance on minority classes. |
TIMERS: Document-level Temporal Relation Extraction (2021.acl-short)
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| Challenge: | Existing methods for temporal relation extraction focus on extracting temporal relations between event pairs present in the same sentence or adjacent sentences, mostly ignoring document-level pairs. |
| Approach: | They propose a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language that leverages rhetorical discourse features and temporal arguments from semantic role labels. |
| Outcome: | The proposed model outperforms previous methods on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to its discourse-level modeling. |
Temporally-Informed Analysis of Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing methods to evaluate text data are rarely reported by taking the timestamp of the document into account. |
| Approach: | They propose methods that make better use of temporally-diverse training data with a focus on named entity recognition. |
| Outcome: | The proposed models make better use of temporally-diverse training data, with a focus on named entity recognition. |