Papers by Aleksandra Edwards
Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)
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| Challenge: | Existing research on text classification models with prompts is limited in scale and lacks understanding of how these methods compare to more established methods. |
| Approach: | They compare the performance of large and smaller language models with prompts to achieve state-of-the-art performance in many NLP tasks. |
| Outcome: | The proposed models outperform the more standard approaches in binary, multiclass, and multilabel tasks in a large scale evaluation of 16 text classification datasets. |
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification (2020.coling-main)
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| Challenge: | Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. |
| Approach: | They compare the performance of a linear classifier based on word embeddings with a pre-trained language model, i.e., BERT, across a wide range of datasets and classification tasks. |
| Outcome: | The proposed method outperforms baselines in standard datasets with large training sets, but in settings with small training datasets it performs better. |