Papers by Donald Zhang
Learning Bias-reduced Word Embeddings Using Dictionary Definitions (2022.findings-acl)
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| Challenge: | Existing word embeddings have undesirable gender, racial, and religious biases . DD-GloVe is a train-time debiasing algorithm that uses dictionary definitions based on word definitions. |
| Approach: | They propose a dictionary-guided loss function that encourages word embeddings to be similar to their relatively neutral dictionary definition representations. |
| Outcome: | The proposed algorithm can learn word embeddings by leveraging dictionary definitions. |
Tomato, Tomahto, Tomate: Do Multilingual Language Models Understand Based on Subword-Level Semantic Concepts? (2025.findings-naacl)
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| Challenge: | a recent study shows that human understanding of text depends on general semantic concepts of words that are robust to their superficial forms. |
| Approach: | They evaluate the accuracy of multilingual multilingual language models based on subword-level semantics . they form "semantic tokens" by merging semantically similar subwords and embeddings based upon the results . |
| Outcome: | The proposed models are able to make predictions on multilingual tasks with different tokenizers and model sizes. |