| Challenge: | Existing work on aspectual classification in English has been motivated as a pre-requisite for Natural Language Understanding (NLU) in cases where temporal reasoning is required. |
| Approach: | They propose to classify English verb phrases into situation aspect categories by gathering crowd-sourced judgements from non-expert, native English participants. |
| Outcome: | The proposed approach uses a crowd-sourced dataset from non-expert, native English participants to examine aspectual entailments in English. |
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Aspectuality Across Genre: A Distributional Semantics Approach (2020.coling-main)
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| Challenge: | Existing studies have focused on the aspectual class of verbs in English for predicting coherence relations in text and imagery, predicting links in entailment graphs and interpreting sign languages. |
| Approach: | They propose to model two elementary aspects of aspectual class, states vs. events, and telic v. atelic events, with distributional semantics. |
| Outcome: | The proposed model outperforms previous work on three datasets and shows that telicity correlates with genre and discourse goals. |
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit increasingly sophisticated linguistic capabilities, yet the extent to which these models reflect human-like cognition versus advanced pattern recognition remains an open question. |
| Approach: | They conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner. |
| Outcome: | The proposed framework can be used to assess the cognitive and linguistic capabilities of large language models (LLMs). |
Do Language Models Have a Common Sense regarding Time? Revisiting Temporal Commonsense Reasoning in the Era of Large Language Models (2023.emnlp-main)
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| 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. |
A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches (2023.eacl-main)
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| Challenge: | Lexical and grammatical aspect plays essential roles in semantic interpretation, but many systems do not address it systematically. |
| Approach: | They propose to model lexical and grammatical aspect using computational approaches . they argue that a good computational understanding of lexic and grammmatical aspects is needed . |
| Outcome: | The proposed models are based on the lexical and grammatical aspect of a situation, the authors argue . they argue that the models need to be able to handle and evaluate the aspect systematically . |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
TIMEDIAL: Temporal Commonsense Reasoning in Dialog (2021.acl-long)
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| Challenge: | Existing studies on pre-trained language models for dialog reasoning fail to understand context correctly. |
| Approach: | They propose to use a crowd-sourced English task and a time-based task to test models' temporal reasoning abilities in dialogs. |
| Outcome: | The proposed task and crowd-sourced English challenge set show that even the best performing models struggle on this task compared to humans. |
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
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. |
Tackling Temporal Questions in Natural Language Interface to Databases (2022.emnlp-industry)
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| Challenge: | Temporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). |
| Approach: | They propose a dataset with accompanied databases supporting temporal questions in NLIDB. |
| Outcome: | The proposed dataset helps two models learn and improve in temporal aspect. |