Search Results for author:
Found 28 papers, 11 papers with code
CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction
We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022.
HypMix: Hyperbolic Interpolative Data Augmentation
Interpolation-based regularisation methods for data augmentation have proven to be effective for various tasks and modalities.
Temporal Graph Analysis of Misinformation Spreaders in Social Media
Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society.
DMix: Distance Constrained Interpolative Mixup
Interpolation-based regularisation methods have proven to be effective for various tasks and modalities.
CAISA@SMM4H’22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods
Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.
OK Boomer: Probing the socio-demographic Divide in Echo Chambers
Social media platforms such as Twitter or Reddit have become an integral part in political opinion formation and discussions, accompanied by potential echo chamber forming.
DMix: Adaptive Distance-aware Interpolative Mixup
Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space.
Pitfalls of Conversational LLMs on News Debiasing
This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task.
Corpus Considerations for Annotator Modeling and Scaling
We introduce a composite embedding approach and show distinct differences in which model performs best as a function of the agreement with a given dataset.
Style Locality for Controllable Generation with kNN Language Models
Nearest neighbor models have been explored for controllable generation but have not examined the use of locality levels.
Challenges of GPT-3-based Conversational Agents for Healthcare
The potential to provide patients with faster information access while allowing medical specialists to concentrate on critical tasks makes medical domain dialog agents appealing.
OpinionConv: Conversational Product Search with Grounded Opinions
This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products.
CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification
The class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset.
Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages.
A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing
We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility.
Nearest Neighbor Language Models for Stylistic Controllable Generation
Recent language modeling performance has been greatly improved by the use of external memory.
Unifying Data Perspectivism and Personalization: An Application to Social Norms
Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators.
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data.
Understanding Interpersonal Conflict Types and their Impact on Perception Classification
Studies on interpersonal conflict have a long history and contain many suggestions for conflict typology.
Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy
Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use.
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
In this paper, we present an approach using diagnosis paths in a medical knowledge graph.
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion
The dataset includes a sufficient amount of stance polarity and intensity labels per user over time and within entire conversational threads, thus making subtle opinion fluctuations detectable both in long term and in short term.
The Impact of Differential Privacy on Group Disparity Mitigation
The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of members of such groups.
Modeling Proficiency with Implicit User Representations
We introduce the problem of proficiency modeling: Given a user's posts on a social media platform, the task is to identify the subset of posts or topics for which the user has some level of proficiency.
Perceived and Intended Sarcasm Detection with Graph Attention Networks
In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user's conversational neighborhood in an interaction graph, to contextualize the interpretation of the post.
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners.
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media
Building on clinical studies, PHASE learns phase-like progressions in users{'} historical Plutchik-wheel-based emotions to contextualize suicidal intent.
Returning the N to NLP: Towards Contextually Personalized Classification Models
Most NLP models today treat language as universal, even though socio- and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience.
