Search Results for author: Morteza Dehghani

Found 14 papers, 3 papers with code

Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation

no code implementations21 Feb 2024 Preni Golazizian, Ali Omrani, Alireza S. Ziabari, Morteza Dehghani

In subjective NLP tasks, where a single ground truth does not exist, the inclusion of diverse annotators becomes crucial as their unique perspectives significantly influence the annotations.

Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning

1 code implementation29 Sep 2023 Ali Omrani, Alireza S. Ziabari, Preni Golazizian, Jeffery Sorensen, Morteza Dehghani

Detecting problematic content, such as hate speech, is a multifaceted and ever-changing task, influenced by social dynamics, user populations, diversity of sources, and evolving language.

Continual Learning

Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model

no code implementations11 Oct 2022 Ali Omrani, Brendan Kennedy, Mohammad Atari, Morteza Dehghani

Existing word embedding debiasing methods require social-group-specific word pairs (e. g., "man"-"woman") for each social attribute (e. g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing.

Attribute

The Moral Foundations Reddit Corpus

no code implementations10 Aug 2022 Jackson Trager, Alireza S. Ziabari, Aida Mostafazadeh Davani, Preni Golazizian, Farzan Karimi-Malekabadi, Ali Omrani, Zhihe Li, Brendan Kennedy, Nils Karl Reimer, Melissa Reyes, Kelsey Cheng, Mellow Wei, Christina Merrifield, Arta Khosravi, Evans Alvarez, Morteza Dehghani

Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests.

domain classification Sentiment Analysis +2

Hate Speech Classifiers Learn Human-Like Social Stereotypes

no code implementations28 Oct 2021 Aida Mostafazadeh Davani, Mohammad Atari, Brendan Kennedy, Morteza Dehghani

Social stereotypes negatively impact individuals' judgements about different groups and may have a critical role in how people understand language directed toward minority social groups.

Fairness

Improving Counterfactual Generation for Fair Hate Speech Detection

no code implementations ACL (WOAH) 2021 Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, Morteza Dehghani

By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.

counterfactual Fairness +2

Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals

no code implementations24 Oct 2020 Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, Morteza Dehghani

Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence.

counterfactual Fairness +2

Contextualizing Hate Speech Classifiers with Post-hoc Explanation

3 code implementations ACL 2020 Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, Morteza Dehghani, Xiang Ren

Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways.

Multilingual Entity, Relation, Event and Human Value Extraction

no code implementations NAACL 2019 Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji

This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.

Event Extraction Relation +1

Acquiring Background Knowledge to Improve Moral Value Prediction

no code implementations16 Sep 2017 Ying Lin, Joe Hoover, Morteza Dehghani, Marlon Mooijman, Heng Ji

In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis.

Value prediction

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