Papers by Daniel Ho
Statistical Uncertainty in Word Embeddings: GloVe-V (2024.emnlp-main)
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| Challenge: | Static word embeddings are ubiquitous in computational social science applications . however, assessing the statistical uncertainty in downstream conclusions remains challenging . |
| Approach: | They propose a method to obtain approximate, easy-to-use, and scalable reconstruction error variance estimates for one of the most widely used word embedding models. |
| Outcome: | The proposed method enables hypothesis testing in key word embedding tasks. |
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (2021.emnlp-main)
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| Challenge: | Pre-trained language models have impressive performance on commonsense inference benchmarks, but their ability to make robust inferences is debated. |
| Approach: | They propose a challenge that evaluates robust commonsense inference despite textual perturbations using commonsensical knowledge bases and probe PTLMs across two different evaluation settings. |
| Outcome: | The proposed procedure evaluates robust commonsense inference despite textual perturbations using commonsensense knowledge bases and probe PTLMs across two evaluation settings. |