Papers by Charu Karakkaparambil James
Continual Neural Topic Model (2026.eacl-long)
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| Challenge: | Continual Neural Topic Models (CoNTMs) learn topic models at subsequent time steps without forgetting what was previously learned. |
| Approach: | They propose a Continual Neural Topic Model which continuously learns topic models at subsequent time steps without forgetting what was previously learned. |
| Outcome: | The proposed model outperforms the dynamic topic model in topic quality and predictive perplexity while being able to capture topic changes online. |
Characterizing Text Datasets with Psycholinguistic Features (2024.findings-emnlp)
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| Challenge: | Existing algorithms for fine-tuning language models on task-specific data are not optimal for all scenarios. |
| Approach: | They propose a framework to fine-tune text models on task-specific data using meta-datasets. |
| Outcome: | The proposed framework evaluates multiple dimensions of text and discourse, producing interpretable, low-dimensional embeddings. |
Evaluating Dynamic Topic Models (2024.acl-long)
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| Challenge: | Existing evaluation measures to evaluate the progression of topics in dynamic topic models (DTMs) are difficult due to their unsupervised nature, but are crucial for detecting trends in time-indexed documents. |
| Approach: | They propose to combine topic quality and temporal consistency to evaluate the progression of topics over time in dynamic topic models. |
| Outcome: | The proposed measure correlates well with human judgment and can be used to identify changing topics and evaluate different models and LLMs. |