Papers by Jinjun Chen
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information (2020.coling-main)
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| Challenge: | Recent studies show word embedding models underestimate similarities between similar words and overestimate similarities between distant words. |
| Approach: | They propose two new word embedding methods that align original and re-fined embeddable spaces to a new refined semantic space. |
| Outcome: | The proposed methods outperform state-of-the-art methods for word representation refinement. |
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction (2022.naacl-main)
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| Challenge: | Existing models are vulnerable to adversarial attacks, but their vulnerability is underexplored. |
| Approach: | They propose to concatenate a perturbed but semantically similar tweet into a model that fools stock prediction models. |
| Outcome: | The proposed method achieves consistent success rates and causes significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet. |