Papers by Vitalii Zhelezniak
Correlations between Word Vector Sets (D19-1)
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| Challenge: | Similarity measures based on word embeddings are easily competing with deep learning and expert-engineered systems on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a new approach to measure word embeddings using pooling operations and correlation coefficients instead of pooling . they also propose centered kernel alignment as a natural generalisation of squared cosine similarity for sets of word vectors. |
| Outcome: | The proposed approach outperforms most recent methods while being much faster and trivial to implement. |
Estimating Mutual Information Between Dense Word Embeddings (2020.acl-main)
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| Challenge: | Word embedding-based similarity measures are among the top-performing methods on unsupervised semantic textual similarity tasks. |
| Approach: | They propose a word embedding similarity measure that is a viable alternative to correlations and gives an excellent signal that correlates well with human judgements of similarity. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on hard NLP tasks. |
Correlation Coefficients and Semantic Textual Similarity (N19-1)
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| Challenge: | Existing research into semantic textual similarity has focused on word embeddings . little attention has been devoted to similarity measures between word embeds - a new study shows . |
| Approach: | They show that cosine similarity is essentially equivalent to the Pearson correlation coefficient for all common word vectors. |
| Outcome: | The proposed model outperforms the existing model on word-level and sentence-level similarity benchmarks. |