Papers by Venkata Subrahmanyan Govindarajan
Help! Need Advice on Identifying Advice (2020.emnlp-main)
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| Challenge: | Pre-trained systems are able to capture advice better than rule-based systems, but advice identification is challenging. |
| Approach: | They analyze a dataset of advice posts on two reddit forums and annotate whether they contain advice. |
| Outcome: | The proposed models show that pre-trained models capture advice better than rule-based systems, but advice identification is challenging. |
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias (2023.findings-acl)
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| Challenge: | Existing work on bias in NLP only considers negative or pejorative language use. |
| Approach: | They propose a revised framing of bias in terms of intergroup social context and its effects on language output. |
| Outcome: | The proposed framework is based on a model of intergroup relationships in English language tweets. |
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion (2023.eacl-main)
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Venkata Subrahmanyan Govindarajan, Katherine Atwell, Barea Sinno, Malihe Alikhani, David I. Beaver, Junyi Jessy Li
| Challenge: | Current studies of bias in NLP rely on identifying (unwanted or negative) bias towards a specific demographic group, but this is not always practical. |
| Approach: | They extrapolate a notion of bias from social science literature to predict interpersonal group relationship (IGR) using interpersonal emotions as an anchor. |
| Outcome: | The proposed model predicts the interpersonal group relationship (IGR) using interpersonal emotions as an anchor. |
The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)
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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
| Challenge: | Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement. |
| Approach: | They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer . |
| Outcome: | The dataset is bundled with the Decomp toolkit (v0.1) both datasets are publicly available at http://decomp.io. |