Papers by Charibeth Cheng
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q&A Corpus (D19-51)
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| Challenge: | Existing studies on DA classification in general contexts have not addressed this problem. |
| Approach: | They constructed a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. |
| Outcome: | The text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines was constructed using a tagset for DA classification . the corpus was composed dominantly of single-label posts, with 34% of the corpuse having multiple intent tags. |
Improving Large-scale Language Models and Resources for Filipino (2022.lrec-1)
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| Challenge: | a new large-scale pretraining corpus for Filipino improves existing resources for low-resource languages . a large dataset is too small and too narrow to create robust models that perform well in modern NLP. |
| Approach: | They propose a large-scale pretraining corpus for Filipino and a new RoBERTa pretraining technique to supplant existing models trained with small corpora. |
| Outcome: | The proposed model improves on existing models for the low-resource Filipino language . the model gains 4.47% test accuracy across three classification tasks with varying difficulty . |
Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
| Outcome: | The proposed method can achieve 91% accuracy on a fake news dataset, reducing error by 14% compared to established baselines. |