Shivashankar Subramanian

Also published as: S. Shivashankar


2021

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Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian | Afshin Rahimi | Timothy Baldwin | Trevor Cohn | Lea Frermann
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.

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Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian | Xudong Han | Timothy Baldwin | Trevor Cohn | Lea Frermann
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Bias is pervasive for NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in isolation, e.g., debiasing with respect to binary gender or race, however many corpora involve multiple such attributes, possibly with higher cardinality. In this paper we argue that a truly fair model must consider ‘gerrymandering’ groups which comprise not only single attributes, but also intersectional groups. We evaluate a form of bias-constrained model which is new to NLP, as well an extension of the iterative nullspace projection technique which can handle multiple identities.

2020

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Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model
Kotaro Kitayama | Shivashankar Subramanian | Timothy Baldwin
Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association

Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.

2019

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Target Based Speech Act Classification in Political Campaign Text
Shivashankar Subramanian | Trevor Cohn | Timothy Baldwin
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific speech acts, such as commissive-action, and present a novel annotated corpus of media releases and speech transcripts from the 2016 Australian election cycle. We show how speech acts and target referents can be modeled as sequential classification, and evaluate several techniques, exploiting contextualized word representations, semi-supervised learning, task dependencies and speaker meta-data.

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Deep Ordinal Regression for Pledge Specificity Prediction
Shivashankar Subramanian | Trevor Cohn | Timothy Baldwin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual annotations. In this paper we collate a novel dataset of manifestos from eleven Australian federal election cycles, with over 12,000 sentences annotated with specificity (e.g., rhetorical vs detailed pledge) on a fine-grained scale. We propose deep ordinal regression approaches for specificity prediction, under both supervised and semi-supervised settings, and provide empirical results demonstrating the effectiveness of the proposed techniques over several baseline approaches. We analyze the utility of pledge specificity modeling across a spectrum of policy issues in performing ideology prediction, and further provide qualitative analysis in terms of capturing party-specific issue salience across election cycles.

2018

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Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Shivashankar Subramanian | Timothy Baldwin | Trevor Cohn
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition — commonly measured by its signature count — based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.

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Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis
Shivashankar Subramanian | Trevor Cohn | Timothy Baldwin
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party’s fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left–right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.

2017

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Joint Sentence-Document Model for Manifesto Text Analysis
Shivashankar Subramanian | Trevor Cohn | Timothy Baldwin | Julian Brooke
Proceedings of the Australasian Language Technology Association Workshop 2017

2016

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Filter and Match Approach to Pair-wise Web URI Linking
S. Shivashankar | Yitong Li | Afshin Rahimi
Proceedings of the Australasian Language Technology Association Workshop 2016