Papers by Katherine Atwell
Measuring Bias and Agreement in Large Language Model Presupposition Judgments (2025.findings-acl)
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| Challenge: | Identifying linguistic bias in text requires the identification of explicit statements and presuppositions . large language models can be used to detect subtle forms of bias with no clear lexical signals . |
| Approach: | They propose to prompt large language models to evaluate presuppositions across texts . they find that LLMs may inadvertently reflect societal biases when identifying presuposed content . |
| Outcome: | The proposed model can be used to detect linguistic biases in text, but its accuracy is unclear . linguistic factors associated with human-model alignment suggest biase influenced by gender and ideology. |
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)
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| Challenge: | Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data. |
| Approach: | They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset. |
| Outcome: | The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text . |
Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)
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| Challenge: | a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content. |
| Approach: | They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french . |
| Outcome: | The proposed dataset highlights the challenges and suggests related research problems . it shows that the proposed model can be used to predict the violated rule . |
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI (2025.coling-industry)
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Yuya Asano, Sabit Hassan, Paras Sharma, Anthony B. Sicilia, Katherine Atwell, Diane Litman, Malihe Alikhani
| Challenge: | Existing ASR correction methods rely on prior user data or named entities . Existing methods based on prior data are not available for goal-oriented dialogues . |
| Approach: | They propose a method that integrates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks into a large language model. |
| Outcome: | The proposed method improves recall and F1 of correction by 34% and 16% while maintaining precision and false positive rate. |
The Role of Context and Uncertainty in Shallow Discourse Parsing (2022.coling-1)
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| Challenge: | Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. |
| Approach: | They hypothesize that context plays an important role in accurate human annotation and add uncertainty measures can improve model accuracy and calibration. |
| Outcome: | The proposed model can be better calibrated by adding uncertainty measures to models with better accuracy and calibration. |
Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue (2024.lrec-main)
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| Challenge: | Large language models (LLMs) are capable of generating well-formed responses, but they struggle in goal-oriented settings. |
| Approach: | They propose a discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. |
| Outcome: | The proposed system reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%. |
Political Ideology and Polarization: A Multi-dimensional Approach (2022.naacl-main)
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| Challenge: | Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. |
| Approach: | They propose a novel approach for the study of ideology based on its left or right positions on the issue being discussed. |
| Outcome: | The proposed method allows for the quantitative and temporal measurement and analysis of polarization as a multidimensional ideological distance. |
Studying and Mitigating Biases in Sign Language Understanding Models (2024.emnlp-main)
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| Challenge: | Using crowd-sourced sign language datasets to reduce performance disparities is critical to addressing potential biases and inequities. |
| Approach: | They use demographic information to study biases that may result from models trained on crowd-sourced sign datasets. |
| Outcome: | The proposed approach reduces performance disparities without decreasing accuracy. |
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)
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| Challenge: | Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models. |
| Approach: | They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it. |
| Outcome: | The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap). |
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion (2023.eacl-main)
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Venkata Subrahmanyan Govindarajan, Katherine Atwell, Barea Sinno, Malihe Alikhani, David I. Beaver, Junyi Jessy Li
| Challenge: | Current studies of bias in NLP rely on identifying (unwanted or negative) bias towards a specific demographic group, but this is not always practical. |
| Approach: | They extrapolate a notion of bias from social science literature to predict interpersonal group relationship (IGR) using interpersonal emotions as an anchor. |
| Outcome: | The proposed model predicts the interpersonal group relationship (IGR) using interpersonal emotions as an anchor. |