Vinodkumar Prabhakaran


2023

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Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling
Gavin Abercrombie | Dirk Hovy | Vinodkumar Prabhakaran
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator’s labels are temporally stable, although the reality is that human judgements are rarely consistent over time. As a subjective annotation task, hate speech labels depend on annotator’s emotional and moral reactions to the language used to convey the message. Studies in Cognitive Science reveal a ‘foreign language effect’, whereby people take differing moral positions and perceive offensive phrases to be weaker in their second languages. Does this affect annotations as well? We conduct an experiment to investigate the impacts of (1) time and (2) different language conditions (English and German) on measurements of intra-annotator agreement in a hate speech labelling task. While we do not observe the expected lower stability in the different language condition, we find that overall agreement is significantly lower than is implicitly assumed in annotation tasks, which has important implications for dataset reproducibility in NLP.

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Distinguishing Address vs. Reference Mentions of Personal Names in Text
Vinodkumar Prabhakaran | Aida Mostafazadeh Davani | Melissa Ferguson | Stav Atir
Findings of the Association for Computational Linguistics: ACL 2023

Detecting named entities in text has long been a core NLP task. However, not much work has gone into distinguishing whether an entity mention is addressing the entity vs. referring to the entity; e.g., John, would you turn the light off? vs. John turned the light off. While this distinction is marked by a vocative case marker in some languages, many modern Indo-European languages such as English do not use such explicit vocative markers, and the distinction is left to be interpreted in context. In this paper, we present a new annotated dataset that captures the address vs. reference distinction in English, an automatic tagger that performs at 85% accuracy in making this distinction, and demonstrate how this distinction is important in NLP and computational social science applications in English language.

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Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)
Sunipa Dev | Vinodkumar Prabhakaran | David Adelani | Dirk Hovy | Luciana Benotti
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

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Building Stereotype Repositories with Complementary Approaches for Scale and Depth
Sunipa Dev | Akshita Jha | Jaya Goyal | Dinesh Tewari | Shachi Dave | Vinodkumar Prabhakaran
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

Measurements of fairness in NLP have been critiqued for lacking concrete definitions of biases or harms measured, and for perpetuating a singular, Western narrative of fairness globally. To combat some of these pivotal issues, methods for curating datasets and benchmarks that target specific harms are rapidly emerging. However, these methods still face the significant challenge of achieving coverage over global cultures and perspectives at scale. To address this, in this paper, we highlight the utility and importance of complementary approaches that leverage both community engagement as well as large generative models, in these curation strategies. We specifically target the harm of stereotyping and demonstrate a pathway to build a benchmark that covers stereotypes about diverse, and intersectional identities. We discuss the two approaches, their advantages and constraints, the characteristics of the data they produce, and finally, their potential to be used complementarily for better evaluation of stereotyping harms.

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SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Akshita Jha | Aida Mostafazadeh Davani | Chandan K Reddy | Shachi Dave | Vinodkumar Prabhakaran | Sunipa Dev
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverage, and are largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies gain hold across the globe. To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, built by utilizing generative capabilities of large language models such as PaLM, and GPT-3, and leveraging a globally diverse rater pool to validate the prevalence of those stereotypes in society. SeeGULL is in English, and contains stereotypes about identity groups spanning 178 countries across 8 different geo-political regions across 6 continents, as well as state-level identities within the US and India. We also include fine-grained offensiveness scores for different stereotypes and demonstrate their global disparities. Furthermore, we include comparative annotations about the same groups by annotators living in the region vs. those that are based in North America, and demonstrate that within-region stereotypes about groups differ from those prevalent in North America.

2022

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Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Aida Mostafazadeh Davani | Mark Díaz | Vinodkumar Prabhakaran
Transactions of the Association for Computational Linguistics, Volume 10

Majority voting and averaging are common approaches used to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. Annotator disagreements may capture important nuances in such tasks that are often ignored while aggregating annotations to a single ground truth. In order to address this, we investigate the efficacy of multi-annotator models. In particular, our multi-task based approach treats predicting each annotators’ judgements as separate subtasks, while sharing a common learned representation of the task. We show that this approach yields same or better performance than aggregating labels in the data prior to training across seven different binary classification tasks. Our approach also provides a way to estimate uncertainty in predictions, which we demonstrate better correlate with annotation disagreements than traditional methods. Being able to model uncertainty is especially useful in deployment scenarios where knowing when not to make a prediction is important.

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BeSt: The Belief and Sentiment Corpus
Jennifer Tracey | Owen Rambow | Claire Cardie | Adam Dalton | Hoa Trang Dang | Mona Diab | Bonnie Dorr | Louise Guthrie | Magdalena Markowska | Smaranda Muresan | Vinodkumar Prabhakaran | Samira Shaikh | Tomek Strzalkowski
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present the BeSt corpus, which records cognitive state: who believes what (i.e., factuality), and who has what sentiment towards what. This corpus is inspired by similar source-and-target corpora, specifically MPQA and FactBank. The corpus comprises two genres, newswire and discussion forums, in three languages, Chinese (Mandarin), English, and Spanish. The corpus is distributed through the LDC.

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Re-contextualizing Fairness in NLP: The Case of India
Shaily Bhatt | Sunipa Dev | Partha Talukdar | Shachi Dave | Vinodkumar Prabhakaran
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus of social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fairness in the context of India. We start with a brief account of the prominent axes of social disparities in India. We build resources for fairness evaluation in the Indian context and use them to demonstrate prediction biases along some of the axes. We then delve deeper into social stereotypes for Region and Religion, demonstrating its prevalence in corpora and models. Finally, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian societal context, bridging technological gaps in NLP capabilities and resources, and adapting to Indian cultural values. While we focus on India, this framework can be generalized to other geo-cultural contexts.

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Underspecification in Scene Description-to-Depiction Tasks
Ben Hutchinson | Jason Baldridge | Vinodkumar Prabhakaran
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Questions regarding implicitness, ambiguity and underspecification are crucial for understanding the task validity and ethical concerns of multimodal image+text systems, yet have received little attention to date. This position paper maps out a conceptual framework to address this gap, focusing on systems which generate images depicting scenes from scene descriptions. In doing so, we account for how texts and images convey meaning differently. We outline a set of core challenges concerning textual and visual ambiguity, as well as risks that may be amplified by ambiguous and underspecified elements. We propose and discuss strategies for addressing these challenges, including generating visually ambiguous images, and generating a set of diverse images.

2021

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Learning to Recognize Dialect Features
Dorottya Demszky | Devyani Sharma | Jonathan Clark | Vinodkumar Prabhakaran | Jacob Eisenstein
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in “He ∅ running”. In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.

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On Releasing Annotator-Level Labels and Information in Datasets
Vinodkumar Prabhakaran | Aida Mostafazadeh Davani | Mark Diaz
Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then flattened to produce a single “ground truth” label or score, through majority voting, averaging, or adjudication. While these approaches may be appropriate in certain annotation tasks, such aggregations overlook the socially constructed nature of human perceptions that annotations for relatively more subjective tasks are meant to capture. In particular, systematic disagreements between annotators owing to their socio-cultural backgrounds and/or lived experiences are often obfuscated through such aggregations. In this paper, we empirically demonstrate that label aggregation may introduce representational biases of individual and group perspectives. Based on this finding, we propose a set of recommendations for increased utility and transparency of datasets for downstream use cases.

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Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
Aida Mostafazadeh Davani | Douwe Kiela | Mathias Lambert | Bertie Vidgen | Vinodkumar Prabhakaran | Zeerak Waseem
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
Lambert Mathias | Shaoliang Nie | Aida Mostafazadeh Davani | Douwe Kiela | Vinodkumar Prabhakaran | Bertie Vidgen | Zeerak Waseem
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

We present the results and main findings of the shared task at WOAH 5 on hateful memes detection. The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the meme and (2) the attack type. 3 teams submitted system description papers. This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.

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Detecting Cross-Geographic Biases in Toxicity Modeling on Social Media
Sayan Ghosh | Dylan Baker | David Jurgens | Vinodkumar Prabhakaran
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

Online social media platforms increasingly rely on Natural Language Processing (NLP) techniques to detect abusive content at scale in order to mitigate the harms it causes to their users. However, these techniques suffer from various sampling and association biases present in training data, often resulting in sub-par performance on content relevant to marginalized groups, potentially furthering disproportionate harms towards them. Studies on such biases so far have focused on only a handful of axes of disparities and subgroups that have annotations/lexicons available. Consequently, biases concerning non-Western contexts are largely ignored in the literature. In this paper, we introduce a weakly supervised method to robustly detect lexical biases in broader geo-cultural contexts. Through a case study on a publicly available toxicity detection model, we demonstrate that our method identifies salient groups of cross-geographic errors, and, in a follow up, demonstrate that these groupings reflect human judgments of offensive and inoffensive language in those geographic contexts. We also conduct analysis of a model trained on a dataset with ground truth labels to better understand these biases, and present preliminary mitigation experiments.

2020

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Proceedings of the Fourth Workshop on Online Abuse and Harms
Seyi Akiwowo | Bertie Vidgen | Vinodkumar Prabhakaran | Zeerak Waseem
Proceedings of the Fourth Workshop on Online Abuse and Harms

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Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020
Vinodkumar Prabhakaran | Zeerak Waseem | Seyi Akiwowo | Bertie Vidgen
Proceedings of the Fourth Workshop on Online Abuse and Harms

In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference. Our aim was to bridge the gap between human rights scholarship and Natural Language Processing (NLP) research communities in tackling online abuse. We report on the discussions that took place, and present an analysis of four key issues which emerged: Problems in tackling online abuse, Solutions, Meta concerns and the Ecosystem of content moderation and research. We argue there is a pressing need for NLP research communities to engage with human rights perspectives, and identify four key ways in which NLP research into online abuse could immediately be enhanced to create better and more ethical solutions.

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Social Biases in NLP Models as Barriers for Persons with Disabilities
Ben Hutchinson | Vinodkumar Prabhakaran | Emily Denton | Kellie Webster | Yu Zhong | Stephen Denuyl
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.

2019

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Proceedings of the Third Workshop on Abusive Language Online
Sarah T. Roberts | Joel Tetreault | Vinodkumar Prabhakaran | Zeerak Waseem
Proceedings of the Third Workshop on Abusive Language Online

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Perturbation Sensitivity Analysis to Detect Unintended Model Biases
Vinodkumar Prabhakaran | Ben Hutchinson | Margaret Mitchell
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language data reflect the public discourse at the time the data was produced, and hence NLP models are susceptible to learning incidental associations around named referents at a particular point in time, in addition to general linguistic meaning. An NLP system designed to model notions such as sentiment and toxicity should ideally produce scores that are independent of the identity of such entities mentioned in text and their social associations. For example, in a general purpose sentiment analysis system, a phrase such as I hate Katy Perry should be interpreted as having the same sentiment as I hate Taylor Swift. Based on this idea, we propose a generic evaluation framework, Perturbation Sensitivity Analysis, which detects unintended model biases related to named entities, and requires no new annotations or corpora. We demonstrate the utility of this analysis by employing it on two different NLP models — a sentiment model and a toxicity model — applied on online comments in English language from four different genres.

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Bias and Fairness in Natural Language Processing
Kai-Wei Chang | Vinodkumar Prabhakaran | Vicente Ordonez
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics and diagnostic information from large amounts of training data. Despite the remarkable accuracy of machine learning in various applications, learning algorithms run the risk of relying on societal biases encoded in the training data to make predictions. This often occurs even when gender and ethnicity information is not explicitly provided to the system because learning algorithms are able to discover implicit associations between individuals and their demographic information based on other variables such as names, titles, home addresses, etc. Therefore, machine learning algorithms risk potentially encouraging unfair and discriminatory decision making and raise serious privacy concerns. Without properly quantifying and reducing the reliance on such correlations, broad adoption of these models might have the undesirable effect of magnifying harmful stereotypes or implicit biases that rely on sensitive demographic attributes.In this tutorial, we will review the history of bias and fairness studies in machine learning and language processing and present recent community effort in quantifying and mitigating bias in natural language processing models for a wide spectrum of tasks, including word embeddings, co-reference resolution, machine translation, and vision-and-language tasks. In particular, we will focus on the following topics:+ Definitions of fairness and bias.+ Data, algorithms, and models that propagate and even amplify social bias to NLP applications and metrics to quantify these biases.+ Algorithmic solutions; learning objective; design principles to prevent social bias in NLP systems and their potential drawbacks.The tutorial will bring researchers and practitioners to be aware of this issue, and encourage the research community to propose innovative solutions to promote fairness in NLP.

2018

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Power Networks: A Novel Neural Architecture to Predict Power Relations
Michelle Lam | Catherina Xu | Vinodkumar Prabhakaran
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the performance of automatically predicting power relations using NLP analysis of social interactions remains wanting. In this paper, we present a novel neural architecture that captures manifestations of power within individual emails which are then aggregated in an order-preserving way in order to infer the direction of power between pairs of participants in an email thread. We obtain an accuracy of 80.4%, a 10.1% improvement over state-of-the-art methods, in this task. We further apply our model to the task of predicting power relations between individuals based on the entire set of messages exchanged between them; here also, our model significantly outperforms the 70.0% accuracy using prior state-of-the-art techniques, obtaining an accuracy of 83.0%.

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Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
Darja Fišer | Ruihong Huang | Vinodkumar Prabhakaran | Rob Voigt | Zeerak Waseem | Jacqueline Wernimont
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

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Detecting Institutional Dialog Acts in Police Traffic Stops
Vinodkumar Prabhakaran | Camilla Griffiths | Hang Su | Prateek Verma | Nelson Morgan | Jennifer L. Eberhardt | Dan Jurafsky
Transactions of the Association for Computational Linguistics, Volume 6

We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops. Relying on the theory of institutional talk, we develop a labeling scheme for police speech during traffic stops, and a tagger to detect institutional dialog acts (Reasons, Searches, Offering Help) from transcribed text at the turn (78% F-score) and stop (89% F-score) level. We then develop speech recognition and segmentation algorithms to detect these acts at the stop level from raw camera audio (81% F-score, with even higher accuracy for crucial acts like conveying the reason for the stop). We demonstrate that the dialog structures produced by our tagger could reveal whether officers follow law enforcement norms like introducing themselves, explaining the reason for the stop, and asking permission for searches. This work may therefore inform and aid efforts to ensure the procedural justice of police-community interactions.

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Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions
Vinodkumar Prabhakaran | Premkumar Ganeshkumar | Owen Rambow
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do — an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.

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Socially Responsible NLP
Yulia Tsvetkov | Vinodkumar Prabhakaran | Rob Voigt
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This tutorial will provide an overview of real-world applications of language technologies and the potential ethical implications associated with them. We will discuss philosophical foundations of ethical research along with state of the art techniques. Through this tutorial, we intend to provide the NLP researcher with an overview of tools to ensure that the data, algorithms, and models that they build are socially responsible. These tools will include a checklist of common pitfalls that one should avoid (e.g., demographic bias in data collection), as well as methods to adequately mitigate these issues (e.g., adjusting sampling rates or de-biasing through regularization). The tutorial is based on a new course on Ethics and NLP developed at Carnegie Mellon University.

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RtGender: A Corpus for Studying Differential Responses to Gender
Rob Voigt | David Jurgens | Vinodkumar Prabhakaran | Dan Jurafsky | Yulia Tsvetkov
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Computational Argumentation Quality Assessment in Natural Language
Henning Wachsmuth | Nona Naderi | Yufang Hou | Yonatan Bilu | Vinodkumar Prabhakaran | Tim Alberdingk Thijm | Graeme Hirst | Benno Stein
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

2016

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A Corpus of Wikipedia Discussions: Over the Years, with Topic, Power and Gender Labels
Vinodkumar Prabhakaran | Owen Rambow
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In order to gain a deep understanding of how social context manifests in interactions, we need data that represents interactions from a large community of people over a long period of time, capturing different aspects of social context. In this paper, we present a large corpus of Wikipedia Talk page discussions that are collected from a broad range of topics, containing discussions that happened over a period of 15 years. The dataset contains 166,322 discussion threads, across 1236 articles/topics that span 15 different topic categories or domains. The dataset also captures whether the post is made by an registered user or not, and whether he/she was an administrator at the time of making the post. It also captures the Wikipedia age of editors in terms of number of months spent as an editor, as well as their gender. This corpus will be a valuable resource to investigate a variety of computational sociolinguistics research questions regarding online social interactions.

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Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing
Vinodkumar Prabhakaran | William L. Hamilton | Dan McFarland | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Learning Structures of Negations from Flat Annotations
Vinodkumar Prabhakaran | Branimir Boguraev
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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A New Dataset and Evaluation for Belief/Factuality
Vinodkumar Prabhakaran | Tomas By | Julia Hirschberg | Owen Rambow | Samira Shaikh | Tomek Strzalkowski | Jennifer Tracey | Michael Arrigo | Rupayan Basu | Micah Clark | Adam Dalton | Mona Diab | Louise Guthrie | Anna Prokofieva | Stephanie Strassel | Gregory Werner | Yorick Wilks | Janyce Wiebe
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Committed Belief Tagging on the Factbank and LU Corpora: A Comparative Study
Gregory Werner | Vinodkumar Prabhakaran | Mona Diab | Owen Rambow
Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)

2014

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Staying on Topic: An Indicator of Power in Political Debates
Vinodkumar Prabhakaran | Ashima Arora | Owen Rambow
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Gender and Power: How Gender and Gender Environment Affect Manifestations of Power
Vinodkumar Prabhakaran | Emily E. Reid | Owen Rambow
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Power of Confidence: How Poll Scores Impact Topic Dynamics in Political Debates
Vinodkumar Prabhakaran | Ashima Arora | Owen Rambow
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

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Power of Confidence: How Poll Scores Impact Topic Dynamics in Political Debates
Vinodkumar Prabhakaran | Ashima Arora | Owen Rambow
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

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Predicting Power Relations between Participants in Written Dialog from a Single Thread
Vinodkumar Prabhakaran | Owen Rambow
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Improving the Quality of Minority Class Identification in Dialog Act Tagging
Adinoyi Omuya | Vinodkumar Prabhakaran | Owen Rambow
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Written Dialog and Social Power: Manifestations of Different Types of Power in Dialog Behavior
Vinodkumar Prabhakaran | Owen Rambow
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Who Had the Upper Hand? Ranking Participants of Interactions Based on Their Relative Power
Vinodkumar Prabhakaran | Ajita John | Dorée D. Seligmann
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Who’s (Really) the Boss? Perception of Situational Power in Written Interactions
Vinodkumar Prabhakaran | Owen Rambow | Mona Diab
Proceedings of COLING 2012

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Annotations for Power Relations on Email Threads
Vinodkumar Prabhakaran | Huzaifa Neralwala | Owen Rambow | Mona Diab
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Social relations like power and influence are difficult concepts to define, but are easily recognizable when expressed. In this paper, we describe a multi-layer annotation scheme for social power relations that are recognizable from online written interactions. We introduce a typology of four types of power relations between dialog participants: hierarchical power, situational power, influence and control of communication. We also present a corpus of Enron emails comprising of 122 threaded conversations, manually annotated with instances of these power relations between participants. Our annotations also capture attempts at exercise of power or influence and whether those attempts were successful or not. In addition, we also capture utterance level annotations for overt display of power. We describe the annotation definitions using two example email threads from our corpus illustrating each type of power relation. We also present detailed instructions given to the annotators and provide various statistics on annotations in the corpus.

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Detecting Power Relations from Written Dialog
Vinodkumar Prabhakaran
Proceedings of ACL 2012 Student Research Workshop

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Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Vinodkumar Prabhakaran | Michael Bloodgood | Mona Diab | Bonnie Dorr | Lori Levin | Christine D. Piatko | Owen Rambow | Benjamin Van Durme
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

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Predicting Overt Display of Power in Written Dialogs
Vinodkumar Prabhakaran | Owen Rambow | Mona Diab
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2010

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Automatic Committed Belief Tagging
Vinodkumar Prabhakaran | Owen Rambow | Mona Diab
Coling 2010: Posters

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Uncertainty Learning Using SVMs and CRFs
Vinodkumar Prabhakaran
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

2009

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Committed Belief Annotation and Tagging
Mona Diab | Lori Levin | Teruko Mitamura | Owen Rambow | Vinodkumar Prabhakaran | Weiwei Guo
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

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