Papers with word embedding association tests
Universal Sentence Encoder for English (D18-2)
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil
| Challenge: | TensorFlow Hub sentence embedding models have good task transfer performance . model variants allow for trade-offs between accuracy and compute resources . |
| Approach: | They propose easy-to-use TensorFlow Hub sentence embedding models with good task transfer performance. |
| Outcome: | The proposed models outperform models without transfer learning and those that use only word-level transfer on a number of NLP tasks. |
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition (2023.findings-emnlp)
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| Challenge: | Existing work has established that a person’s demographics and speech style affect how well speech processing models perform for them. |
| Approach: | They propose a method to detect bias in pre-trained models by using word embedding association tests in natural language processing to quantify bias in models' representations of different concepts. |
| Outcome: | The proposed method detects bias in pre-trained models and can have real-world effects. |