Venkata Subrahmanyan Govindarajan


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Counterfactually Probing Language Identity in Multilingual Models
Anirudh Srinivasan | Venkata Subrahmanyan Govindarajan | Kyle Mahowald
Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)

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Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
Venkata Subrahmanyan Govindarajan | David Beaver | Kyle Mahowald | Junyi Jessy Li
Findings of the Association for Computational Linguistics: ACL 2023

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts — thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model’s usage of specificity is inconclusive.

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How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Venkata Subrahmanyan Govindarajan | Katherine Atwell | Barea Sinno | Malihe Alikhani | David I. Beaver | Junyi Jessy Li
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigating negative bias, and having a clear notion of the targeted group is necessary, it is not always practical. In this work we extrapolate to a broader notion of bias, rooted in social science and psychology literature. We move towards predicting interpersonal group relationship (IGR) - modeling the relationship between the speaker and the target in an utterance - using fine-grained interpersonal emotions as an anchor. We build and release a dataset of English tweets by US Congress members annotated for interpersonal emotion - the first of its kind, and ‘found supervision’ for IGR labels; our analyses show that subtle emotional signals are indicative of different biases. While humans can perform better than chance at identifying IGR given an utterance, we show that neural models perform much better; furthermore, a shared encoding between IGR and interpersonal perceived emotion enabled performance gains in both tasks.


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Help! Need Advice on Identifying Advice
Venkata Subrahmanyan Govindarajan | Benjamin Chen | Rebecca Warholic | Katrin Erk | Junyi Jessy Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research.

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The Universal Decompositional Semantics Dataset and Decomp Toolkit
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
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification—with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at