| Challenge: | Temporal common sense is crucial for understanding natural language, but its acquisition is challenging . human annotation on such concepts is costly and often not made explicit in text . |
| Approach: | They propose a method that exploits explicit and implicit mentions of temporal common sense to build a temporal similarity language model. |
| Outcome: | The proposed model gives better predictions of various dimensions of temporal common sense than the standard BERT. |
Similar Papers
Fine-Grained Temporal Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that deep contextualized language models (LMs) can extract temporal relations between events and time expressions. |
| Approach: | They propose a temporal relation extraction technique which extracts temporal relations between events and time expressions. |
| Outcome: | The proposed method significantly improves temporal dependency parsing, the authors show . their work compares the proposed method to other methods and shows where they may fail . |
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding (2024.acl-long)
Copied to clipboard
| Challenge: | Existing research in complex event analysis has made significant strides but is constrained by inadequate natural language processing techniques. |
| Approach: | They propose a novel approach using Large Language Models to extract and analyze the event chain within TCE, characterized by their key points and timestamps. |
| Outcome: | The proposed model performs comparable to models with long context window and retrieval-augmented generation method in three distinct tasks . |
Toward Building a Language Model for Understanding Temporal Commonsense (2022.aacl-srw)
Copied to clipboard
| Challenge: | Pre-trained language models such as BERT are still poor in temporal reasoning . commonsense reasoning is crucial for natural language processing (NLP) |
| Approach: | They propose to use multi-step fine-tuning and masked language modeling to predict mangled temporal indicators that are crucial for commonsense reasoning. |
| Outcome: | The proposed model improves performance on multiple time-related tasks. |
Discourse-Aware In-Context Learning for Temporal Expression Normalization (2024.naacl-short)
Copied to clipboard
| 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. |
| Outcome: | The proposed model performs better in non-standard settings by dynamically including relevant examples during inference. |
Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
| Approach: | They propose a framework to improve LLMs’ temporal relation extraction capabilities using context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
| Outcome: | The proposed framework improves LLMs’ extraction capabilities by focusing on context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to effectively retain and reason about temporal information remains limited. |
| Approach: | They propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. |
| Outcome: | The proposed methods improve performance and reduce incorrect outputs. |
Temporal Reasoning in Natural Language Inference (2020.findings-emnlp)
Copied to clipboard
| Challenge: | We use five new natural language inference (NLI) datasets focused on temporal reasoning. |
| Approach: | They introduce five new natural language inference datasets focused on temporal reasoning. |
| Outcome: | The proposed models capture the temporal reasoning of four existing datasets. |
Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |
Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Temporal reasoning is a vital component of human communication and understanding, yet remains an underexplored area within the context of Large Language Models (LLMs). |
| Approach: | They propose to use 3 prompting strategies to evaluate 8 different LLMs across 6 datasets and 2 Code Generation LMs to perform the analysis. |
| Outcome: | The proposed models perform better on NLP tasks than the standard models on the same dataset. |