Randomized Deep Structured Prediction for Discourse-Level Processing

Widmoser, Manuel; Pacheco, María Leonor; Honorio, Jean; Goldwasser, Dan

doi:10.18653/v1/2021.eacl-main.100

“…This hybrid modeling paradigm allow us to leverage expressive textual encoders, and to introduce contextualizing information and model different interdependent decisions. SRL methods have proven effective to model domains with limited supervision (Johnson and Goldwasser, 2018;Subramanian et al, 2018), and approaches that combine neural networks and SRL have shown consistent performance improvements (Widmoser et al, 2021;Roy et al, 2021).…”

Section: Inter-annotator Agreementmentioning

confidence: 99%

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

Pacheco¹,

Islam²,

Mahajan³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.covid vaccine, covid vaccination, covid vaccine tyranny, covid vaccine oppression, covid vaccine mandate, covid vaccine conspiracy, covid vaccine anti-vax, covid vaccine religion, covid vaccine satan, covid vaccine god, covid vaccine jesus, covid vaccine islam, covid vaccine muslim, covid vaccine christianity, covid vaccine christian, covid vaccine hindu, covid vaccine jews, covid vaccine catholic, covid vaccine buddhism, covid vaccine religious, covid vaccine biden failure, covid vaccine passport, covid vaccine loyalty, covid vaccine cheating, covid vaccine freedom, covid vaccine betrayal, covid vaccine liberty, covid vaccine black people, covid vaccine propaganda, covid vaccine hesitancy, covid vaccine hesitant, covid vaccine microchip, covid vaccine bill, covid vaccine pregnancy, covid vaccine pregnant, covid vaccine approval, covid vaccine biden, covid vaccine fda, covid vaccine cdc, covid vaccine fauci, Covid-19 china, vaccine passport, vaccination mandate, covid vaccine death, covid vaccine military, experimental covid vaccine, covid vaccine authorization, vaccine oppression, vaccine satan, covid vaccine bill gates, covid vaccine side effect, covid vaccine adverse events

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“…This hybrid modeling paradigm allow us to leverage expressive textual encoders, and to introduce contextualizing information and model different interdependent decisions. SRL methods have proven effective to model domains with limited supervision (Johnson and Goldwasser, 2018;Subramanian et al, 2018), and approaches that combine neural networks and SRL have shown consistent performance improvements (Widmoser et al, 2021;Roy et al, 2021). Following the conventions of statistical relational learning models, we use horn-clauses of the form p 0 ∧ p 1 ∧ ... ∧ p n ⇒ h to describe relational properties.…”

Section: Joint Probabilistic Modelmentioning

confidence: 99%

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

Pacheco,

Islam,

Mahajan

et al. 2022

Preprint

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The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and finegrained entity level moral sentiment analysis.We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the lowsupervision settings.

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Identifying Morality Frames in Political Tweets using Relational Learning

Roy¹,

Pacheco²,

Goldwasser³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and highquality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.

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Randomized Deep Structured Prediction for Discourse-Level Processing

Cited by 5 publications

References 23 publications

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

Identifying Morality Frames in Political Tweets using Relational Learning

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