Papers by Qitong Wang
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph (2022.coling-1)
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| Challenge: | Existing models that learn word embeddings rely on a large corpus of data . however, these models require massive time and space for data pre-processing and training . |
| Approach: | They propose a model that learns word embeddings utilizing only dictionaries and thesauri . they exploit a new context-focused loss model that models transitive relationships between word pairs . |
| Outcome: | The proposed model reaches the state-of-art on multiple word similarity and relatedness benchmarks. |
Multi-Sense Embeddings for Language Models and Knowledge Distillation (2025.findings-acl)
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| Challenge: | Transformer-based large language models generate different representations for the same token depending on context . however, words and tokens typically have only a limited number of senses . a knowledge distillation method can be used to learn a smaller student model . |
| Approach: | They propose a multi-sense embedding method that uses a clustering algorithm to generate a sense embeddable dictionary. |
| Outcome: | The proposed method offers significant space and inference time savings while maintaining competitive performance. |