| Challenge: | Existing methods for automatic text dating overlook the evolution of word meanings in texts spanning long periods. |
| Approach: | They propose a temporal-aware text representation that dynamically captures both semantic variance and invariance. |
| Outcome: | The proposed approach outperforms existing methods on two diachronic datasets. |
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| Challenge: | Existing models that ignore the temporal relatedness of documents are time-agnostic and therefore fail to perform in automatic text dating. |
| Approach: | They propose a supervised fine-tuning model for automatic text dating that captures temporal semantic information and uses a contrastive learning-based approach to model two types of temporal relations of diachronic documents. |
| Outcome: | The proposed model outperforms state-of-the-art models on two diachronic corpora and captures temporal semantic information. |
Time-aware Prompting for Text Generation (2022.findings-emnlp)
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| Challenge: | a new study investigates the effects of incorporating timestamps into generation systems . textual prompts focus more on non-temporal information and are less sensitive to given timestams . |
| Approach: | They propose a data-to-text generation dataset that includes chronologically ordered revisions of biographical articles from English Wikipedia. |
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Time-Aware Language Modeling for Historical Text Dating (2023.findings-emnlp)
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| Challenge: | Existing approaches to automatic text dating ignore diachronic change of words, which may affect the efforts of text modeling. |
| Approach: | They propose a time-aware language model to learn temporal word representations by transferring language models of general domains to those of time-specific ones and build a hierarchical modeling approach to represent diachronic documents by encoding them with temporal representations. |
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Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
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The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)
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| Challenge: | Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks. |
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Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025.findings-naacl)
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| Challenge: | Prompt tuning is effective in extracting knowledge from foundation models, but its effectiveness is uncertain. |
| Approach: | They propose a parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. |
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Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)
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| Challenge: | Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research. |
| Approach: | They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function. |
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ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals (2026.findings-acl)
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Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022.naacl-main)
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| Challenge: | Existing systems that generate *flashbacks* are monotonic and lack explicit guidance on how to insert them. |
| Approach: | They propose to use event temporal orders to encode events as temporal prompts . they leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines . |
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Discourse-Aware In-Context Learning for Temporal Expression Normalization (2024.naacl-short)
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| Challenge: | Temporal expression (TE) normalization is a well-studied problem, but upcoming machine learning approaches suffer from a lack of labeled data. |
| Approach: | They propose to use in-context learning to inject task, document, and example information into a large language model for temporal expression normalization. |
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