Papers by Amith Ananthram
Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)
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| Challenge: | Existing methods for general purpose relation extraction use a fixed set of predetermined relations, but research has shifted to the identification of unseen relations in any language. |
| Approach: | They propose a method for collecting high quality relation training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. |
| Outcome: | The proposed method achieves comparable results to the current state-of-the-art when trained on a smaller multilingual encoder . |
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies (2022.findings-emnlp)
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| Challenge: | Practitioners from many disciplines use expert-crafted taxonomies to make sense of large, unlabeled corpora. |
| Approach: | They propose a weakly supervised algorithm for seeded hierarchical clustering that fits unlabeled data to taxonomies using a small set of labeled examples. |
| Outcome: | The proposed algorithm outperforms baselines on three real-world datasets. |
Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence (2023.findings-acl)
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| Challenge: | Existing fact-checking benchmarks require systems to verify claims from everyday text against evidence from scientific journal articles. |
| Approach: | They propose a benchmark system that checks claims from news against scientific journal articles and veracity labels. |
| Outcome: | The new benchmark achieves F1 scores of 76.99 and 69.90 on both a fact-checking specific system and GPT-3.5, respectively. |
FeelingBlue: A Corpus for Understanding the Emotional Connotation of Color in Context (2023.tacl-1)
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| Challenge: | Experimental results shed light on the emotional connotation of color in context . color is a powerful tool for conveying emotion across cultures . |
| Approach: | They propose a multimodal dataset for exploring the emotional connotation of color as mediated by line, stroke, texture, shape, and language. |
| Outcome: | The proposed model sheds light on the emotional connotation of color in context and the potential for future studies. |
Data Caricatures: On the Representation of African American Language in Pretraining Corpora (2025.acl-long)
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Nicholas Deas, Blake Vente, Amith Ananthram, Jessica A Grieser, Desmond U. Patton, Shana Kleiner, James R. Shepard Iii, Kathleen McKeown
| Challenge: | Recent work in linguistics and NLP has investigated the quantity and quality of AAL representation in pretraining corpora. |
| Approach: | They examine the quantity and quality of African American Language (AAL) representation in pretraining corpora. |
| Outcome: | The results show that AAL is underrepresented in all evaluated corpora compared to US demographics . they also show that most automated filters are more likely to conserve white Mainstream English (WME) texts over AAL . |
Social Orientation: A New Feature for Dialogue Analysis (2024.lrec-main)
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| Challenge: | Existing studies on social orientations in dialogues show they improve performance in low-resource settings. |
| Approach: | They propose to use social orientation tags to model dialogue outcomes . they introduce a new set of dialogue utterances machine-labeled with social orientation tag. |
| Outcome: | The proposed model improves on English and Chinese language benchmarks and shows that social orientation tags explain the outcomes of social interactions when used in neural models. |