| Challenge: | Prior studies have focused on the application of fixed-size contiguous citation contexts or manually curated citation contextual contexts. |
| Approach: | They propose an automated unsupervised approach for the selection of a dynamic-size and potentially non-contiguous citation context based on transformer-based document representations and embedding similarities. |
| Outcome: | The proposed model improves on the domain-specific and multi-disciplinary datasets, irrespective of the dataset's domain. |
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MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting (2022.naacl-main)
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| Challenge: | Citation context analysis (CCA) is an important task in natural language processing that studies how and why scholars discuss each other’s work. |
| Approach: | They propose to use a dataset of 12.6K citation contexts from 1.2K computational linguistics papers to model three important CCA phenomena. |
| Outcome: | The proposed dataset contains 12.6K citation contexts from 1.2K computational linguistics papers and can model these phenomena. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
Larger-Context Tagging: When and Why Does It Work? (2021.naacl-main)
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| Challenge: | Existing tagging systems that use sentence-level data are not well understood. |
| Approach: | They propose a larger-context approach to tagging tasks that incorporates contextual information into existing tapping systems. |
| Outcome: | The proposed aggregators improve on four tagging tasks and 13 datasets. |
What Context Features Can Transformer Language Models Use? (2021.acl-long)
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| Challenge: | Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. |
| Approach: | They propose to use lexical and structural information to ablate usable information in transformer language models. |
| Outcome: | The proposed model improves when conditioning on contexts of thousands of previous tokens. |
FineCite: A Novel Approach For Fine-Grained Citation Context Analysis (2025.findings-acl)
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| Challenge: | citation context analysis (CCA) studies the role and purpose of citations in scientific discourse. |
| Approach: | They construct a first comprehensive context definition based on semantic properties of citing text . they use fine-grained semantic properties to evaluate the definition . |
| Outcome: | The proposed definition shows improvements of up to 25% over state-of-the-art methods. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors (2020.emnlp-main)
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| Challenge: | Recent research has shown that contextualized models generate dynamic embeddings for words in context, but static embedds are often overlooked in this trend towards contextualized modeling. |
| Approach: | They propose a method that learns a transformation through static anchors and requires only another pre-trained model. |
| Outcome: | The proposed method improves a range of benchmark tasks that test contextual variations of meaning across different usages of a word and across different words as they are used in context. |
The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
| Approach: | They explore the impact of global document context on Named Entity Recognition . they find that correctly retrieving global document contextual has a greater impact . |
| Outcome: | The proposed model can retrieve global context better than leveraging local context . authors say the model can push the state of the art further . |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
In-Context Learning with Long-Context Models: An In-Depth Exploration (2025.naacl-long)
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| Challenge: | In-context learning is limited by context length, but it can be used for many tasks. |
| Approach: | They study the behavior of in-context learning at an extreme context length . example retrieval shows excellent performance at low context lengths but has diminished gains . |
| Outcome: | The proposed model can perform many tasks with reasonable accuracy when a few examples are provided in-context. |