# A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches

arXiv: https://arxiv.org/abs/2208.09012 · HTML: https://mlsanity.com/paper/2208.09012/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 160 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [To Test Machine Comprehension, Start by Defining Comprehension](https://mlsanity.com/paper/2005.01525/) (2020)
- [(Re)construing Meaning in NLP](https://mlsanity.com/paper/2005.09099/) (2020)
- [Zero-Shot Activity Recognition with Verb Attribute Induction](https://mlsanity.com/paper/1707.09468/) (2017)
- [Improving Event Duration Prediction via Time-aware Pre-training](https://mlsanity.com/paper/2011.02610/) (2020)
- [Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements](https://mlsanity.com/paper/1901.11429/) (2019)
- [Data Augmentation for Hypernymy Detection](https://mlsanity.com/paper/2005.01854/) (2020)
- [Decomposing and Recomposing Event Structure](https://mlsanity.com/paper/2103.10387/) (2021)
- [Aspectuality Across Genre: A Distributional Semantics Approach](https://mlsanity.com/paper/2011.00345/) (2020)
- [Building Context-aware Clause Representations for Situation Entity Type Classification](https://mlsanity.com/paper/1809.07483/) (2018)
- [Discriminative and Generative Transformer-based Models For Situation Entity Classification](https://mlsanity.com/paper/2109.07434/) (2021)

## Built on by (most cited first, 5 of 5)

- [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2502.12378/) (2025)
- [Dynamic Relation Inference via Verb Embeddings](https://mlsanity.com/paper/2503.13021/) (2025)
- [Enhancing Structured Meaning Representations with Aspect Classification](https://mlsanity.com/paper/2603.24797/) (2026)
- [A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research](https://mlsanity.com/paper/2605.16654/) (2026)
- [Modeling the Developmental Shift in Telicity Acquisition](https://mlsanity.com/paper/2609.17996/) (2026)

## Abstract (arXiv)

Aspectual meaning refers to how the internal temporal structure of situations is presented. This includes whether a situation is described as a state or as an event, whether the situation is finished or ongoing, and whether it is viewed as a whole or with a focus on a particular phase. This survey gives an overview of computational approaches to modeling lexical and grammatical aspect along with intuitive explanations of the necessary linguistic concepts and terminology. In particular, we describe the concepts of stativity, telicity, habituality, perfective and imperfective, as well as influential inventories of eventuality and situation types. We argue that because aspect is a crucial component of semantics, especially when it comes to reporting the temporal structure of situations in a precise way, future NLP approaches need to be able to handle and evaluate it systematically in order to achieve human-level language understanding.