Papers by William Sethares
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains. (C18-1)
Copied to clipboard
| Challenge: | Standard word embedding algorithms learn vector representations from large corpora of text documents in unsupervised fashion. |
| Approach: | They propose an algorithm that learns word embeddings jointly with a classifier . their algorithm leverages document label information to learn vector representations of words . |
| Outcome: | The proposed algorithm has superior performance on domains with limited data compared to other methods. |
A New View of Multi-modal Language Analysis: Audio and Video Features as Text “Styles” (2021.eacl-main)
Copied to clipboard
| Challenge: | Fig. 1 shows how style-transferred multi-modal features can be used in sentiment analysis and emotion recognition. |
| Approach: | They propose to use adaptive normalization to impose style onto text to learn richer representations for multi-modal utterances. |
| Outcome: | The proposed model achieves performance on par with state-of-the-art but using less than a third of the model parameters. |
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |
Learning Label Hierarchy with Supervised Contrastive Learning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing approaches to supervised contrastive learning treat each class as independent and therefore consider all classes to be equally important. |
| Approach: | They propose a family of Label-Aware SCL methods that incorporate hierarchical information to SCL by leveraging similarities between classes. |
| Outcome: | The proposed method outperforms baseline supervised approaches on three datasets. |