Papers by Candace Ross
Improving Model Evaluation using SMART Filtering of Benchmark Datasets (2025.naacl-long)
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| Challenge: | Creating high quality human-annotated datasets is difficult due to dataset saturation. |
| Approach: | They propose a method to filter a subset of test examples from existing benchmarks by removing less informative and lower quality examples. |
| Outcome: | The proposed method reduces dataset size by 48% while increasing Pearson correlation with rankings from ChatBot Arena. |
Measuring Social Biases in Grounded Vision and Language Embeddings (2021.naacl-main)
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| Challenge: | Existing methods to measure social biases in word embeddings are limited to visually grounded word embeds . a new study generalizes word embedment associations to visually ground word embeddas . |
| Approach: | They generalize word embeddings' biases to visually grounded word embeds . they propose two generalizations that answer questions about how biase, language, and vision interact . |
| Outcome: | The proposed measures are applied to a new dataset that includes 10,228 images from COCO, Conceptual Captions, and Google Images. |
Grounding language acquisition by training semantic parsers using captioned videos (D18-1)
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| Challenge: | a new method for parsing sentences using captioned videos is being developed . we use video clips to ground the semantics of language, but without annotations . |
| Approach: | They develop a semantic parser that is trained in a grounded setting using captioned videos . they use a corpus of sentences paired with videos without other annotations to train it . |
| Outcome: | The proposed parser recovers the meaning of English sentences despite no annotations . learning a grounded semantic parsers can expand the range of data that parseurs can be trained on . |
Perturbation Augmentation for Fairer NLP (2022.emnlp-main)
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| Challenge: | Unwanted and often harmful social biases are becoming more salient in NLP research. |
| Approach: | They propose to train a neural perturbation model that rewrites demographic references in text to make them more fair. |
| Outcome: | The proposed model outperforms heuristic alternatives on a large dataset of human annotated text perturbations. |