Papers by Rebecca Hwa
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020.coling-main)
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| Challenge: | a study examines the impact of political ideology biases in training data . topic detection methods may contain or propagate certain biase resulting in a skewed data collection . |
| Approach: | They propose to learn a text representation that is invariant to political ideology while still judging topic relevance. |
| Outcome: | The proposed model can be invariant to political ideology while still judging topic relevance. |
Heuristically Informed Unsupervised Idiom Usage Recognition (D18-1)
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| Challenge: | Existing models for idiom usage recognition have failed to recognize usages without annotated examples. |
| Approach: | They propose an unsupervised method for recognizing the intended usages of idioms by using distributional semantics to identify literal usages. |
| Outcome: | The proposed method performs competitively against supervised methods. |
Decoding Symbolism in Language Models (2023.acl-long)
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| Challenge: | Existing language models can be used to decode symbolism, but they are biased in pre-trained corpora. |
| Approach: | They propose to use language models to decode symbols by re-ranking pre-trained models. |
| Outcome: | The proposed framework shows that pre-trained models can mitigate the bias and improve performance to be on par with human models. |
Semantic Pleonasm Detection (N18-2)
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| Challenge: | Pleonasms are words that are redundant. |
| Approach: | They propose an annotated corpus of semantic pleonasms and compare it against other corpus resources. |
| Outcome: | The proposed corpus is validated with interannotator agreement analyses and compares it with other corpus resources. |