Papers by Kevin Yen
TNT: Text Normalization based Pre-training of Transformers for Content Moderation (2020.emnlp-main)
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| Challenge: | Language model pre-training (self-supervised or unsupervised learning) has been widely used in a multitude of language processing tasks such as named entity recognition, sentiment analysis, question answering and content moderation. |
| Approach: | They propose a new language pre-training model TNT for content moderation that uses a combination of masking strategy and text normalization to learn from text. |
| Outcome: | The proposed model outperforms baselines on hate speech classification task and is a potential approach to misspelling correction. |
BERT-Beta: A Proactive Probabilistic Approach to Text Moderation (2021.emnlp-main)
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| Challenge: | Existing approaches to text moderation are reactive and do not account for user generated content. |
| Approach: | They propose a text toxicity propensity model to characterize extent to which a user generated text attracts toxic comments and introduce a beta regression model to do the probabilistic modeling. |
| Outcome: | The proposed model performs well in comprehensive experiments and is scalable. |
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense (2022.findings-acl)
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| Challenge: | ANTHRO extracts over 600K human-written text perturbations and leverages them for realistic adversarial attacks. |
| Approach: | They propose an adversarial text manipulation algorithm that inductively extracts over 600K human-written text perturbations and leverages them for realistic adversarials. |
| Outcome: | The proposed algorithm outperforms the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by layperson and professional human workers. |
HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020.emnlp-main)
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| Challenge: | HABERTOR model is a highly efficient and effective alternative to BERT for the hatespeech classification task. |
| Approach: | They propose to modify BERT's HABERTOR model to generate its own vocabularies and pre-trained it using the largest scale hatespeech dataset. |
| Outcome: | The proposed model is faster, more efficient and more robust than existing methods for hatespeech classification. |