Papers by Dhiman Goswami
Native Language Identification in Texts: A Survey (2024.naacl-long)
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| Challenge: | Native language identification is the task of automatically identifying an author’s native language (L1) based on their second language production. |
| Approach: | They present a survey of native language identification applied to texts . authors describe several text representations and computational techniques used in the task . |
| Outcome: | The proposed task has been widely studied for both text and speech, particularly for L2 English due to the availability of suitable corpora. |
Tracing L1 Interference in English Learner Writing: A Longitudinal Corpus with Error Annotations (2025.emnlp-main)
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Poorvi Acharya, J. Elizabeth Liebl, Dhiman Goswami, Kai North, Marcos Zampieri, Antonios Anastasopoulos
| Challenge: | high-quality learner corpora are rarely available for studies of second language acquisition and language transfer. |
| Approach: | They propose to curate a corpus of adult learners with longitudinal data that includes 15 different L1s. |
| Outcome: | The proposed corpus contains 687 texts written by adult learners in the USA . authors show that the corpus can be used to explore language learning trajectories over time. |
NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey (2026.findings-eacl)
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| Challenge: | Social media platforms such as X (formerly Twitter), Facebook, and Reddit generate user-generated content. |
| Approach: | They propose a framework to assess privacy risks in social media by evaluating vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance. |
| Outcome: | The proposed framework assesses privacy risks across six dimensions . it achieves F1-scores of 0.58–0.84, but incurs 1% - 23% drop under fine-tuning . |
Multilingual Native Language Identification with Large Language Models (2025.naacl-srw)
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| Challenge: | Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of individuals based on their second language production. |
| Approach: | They evaluated the performance of several LLMs on non-English NLI corpora compared to traditional statistical machine learning models and language-specific BERT-based models. |
| Outcome: | The proposed models outperform statistical models and language-specific BERT-based models on English, Italian, Norwegian, and Portuguese. |