Papers by Allison Lahnala
Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent work on controllable text generation has shown promise in successfully altering such text attributes. |
| Approach: | They propose to use empathetic data to reduce the toxicity of generated text by strategically sampling data based on empathy scores. |
| Outcome: | The proposed model significantly reduces the size of fine-tuning data to 7.5-30k samples while making significant improvements over state-of-the-art toxicity mitigation. |
Exploring Self-Identified Counseling Expertise in Online Support Forums (2021.findings-acl)
Copied to clipboard
Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
| Challenge: | Increasing number of people engage in online health forums, making it important to understand the quality of the advice they receive. |
| Approach: | They examine the role of expertise in responses to help-seeking posts . they find that a classifier can distinguish between peer and self-identified mental health professionals' interactions . |
| Outcome: | The findings show that experts' language use differs between groups, and that their comments engage the support-seeker further. |
A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Empathy recognition and empathetic response generation tasks are well-established research directions, but there is little clarity on what empathy is and how it is being operationalized. |
| Approach: | They argue that current directions will benefit from a clear conceptualization that includes operationalizing cognitive empathy components. |
| Outcome: | The proposed framework will help to define and operationalize empathy in natural language processing. |
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing models that model fine-grained opinion shifts of social media users are lacking . lack of publicly available datasets for this task presents a major challenge . |
| Approach: | They propose an annotated social media opinion dataset that provides a model for subtle opinion fluctuations and fine-grained stances. |
| Outcome: | The proposed dataset is comparable to the annotations of experts and non-experts. |
Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent work has explored the use of personal information in the form of persona sentences to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. |
| Approach: | They categorize self-disclosures and use them to build annotator models for predicting judgments of social norms by analyzing comments from original post. |
| Outcome: | The proposed model improves the model and its ability to predict annotator labels. |
The Practical Impacts of Theoretical Constructs on Empathy Modeling (2025.emnlp-main)
Copied to clipboard
| Challenge: | Empathy operationalizations in NLP are varied, with some having specific behaviors and properties, while others are more abstract. |
| Approach: | They analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings and characterize them as direct, abstract, or adjacent. |
| Outcome: | The proposed models show that they are more transferable than other models. |