| Challenge: | a crucial piece of information regarding events is their duration, a rarely mentioned attribute . core tasks such as temporal understanding and reasoning would benefit from knowing the expected duration of events. |
| Approach: | They introduce aspectual features that capture deeper linguistic information . they also experiment with neural networks to predict event durations . |
| Outcome: | The proposed models capture deeper linguistic information than previous work and provide useful clues. |
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Temporal Reasoning in Natural Language Inference (2020.findings-emnlp)
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| 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. |
Improving Event Duration Prediction via Time-aware Pre-training (2020.findings-emnlp)
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| Challenge: | Understanding duration of event expressed in text is crucial task in NLP . current methods focus on developing features and cannot utilize external textual knowledge. |
| Approach: | They propose two models that incorporate external knowledge by reading temporal-related news sentences. |
| Outcome: | The proposed models outperform baseline models and capture duration information more accurately. |
Fine-Grained Temporal Relation Extraction (P19-1)
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| 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. |
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Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
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| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
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Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
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Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
| Challenge: | Existing models for temporal ordering of events rely on pretrained representations, transfer and multitask learning, and self-training techniques. |
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Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
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Temporal Token Matters: Investigating and Interpreting the Consistency of Temporal Ordering in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit notable deficiencies in temporal reasoning . phrasing changes can lead LLMs to produce inconsistent outputs . |
| Approach: | They investigate the mechanistic interpretability of temporal ordering within event temporal reasoning . they identify a sparse subset of attention heads that are causally responsible for reasoning outcomes . |
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Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)
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| Challenge: | Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored. |
| Approach: | They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools. |
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Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
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Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding (2024.acl-long)
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| 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 . |