Papers by Muhammad Imran
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs. |
| Approach: | They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text. |
| Outcome: | The proposed pipeline outperforms existing parsers and baselines in syntactic analysis. |
IDRISI-RA: The First Arabic Location Mention Recognition Dataset of Disaster Tweets (2023.acl-long)
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| Challenge: | a low resource language such as Arabic is understudied for geolocation extraction . a recent study found that geolocation is underutilized for low resource languages such as arabic . |
| Approach: | They propose a publicly-available Arabic Location Mention Recognition dataset . it provides human- and automatically-labeled versions of tweets in order of thousands and millions of tweet . |
| Outcome: | The proposed dataset provides human- and automatically-labeled versions in order of thousands and millions of tweets. |
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets (2020.coling-main)
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| Challenge: | Despite the widespread use of Twitter during emergencies, the majority of tweets do not have geoinformation. |
| Approach: | They propose to use Twitter to train location mention recognition models using different training settings. |
| Outcome: | The results show that training on near or far-away events boosts the performance compared to training on distant events. |
Domain Adaptation with Adversarial Training and Graph Embeddings (P18-1)
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| Challenge: | Existing models for deep neural networks can handle data distributions between source and target domains, but they must deal with data distribution drifts. |
| Approach: | They propose a model that leverages unlabeled and labeled data from a related domain to deal with distribution drifts. |
| Outcome: | The proposed model improves over baselines on two real-world disaster datasets. |