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Found 21 papers, 8 papers with code
Leveraging Similar Users for Personalized Language Modeling with Limited Data
Personalized language models are designed and trained to capture language patterns specific to individual users.
Knowledge Enhanced Reflection Generation for Counseling Dialogues
We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses.
Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context
We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills.
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.
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.
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.
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.
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.
Exploring Self-Identified Counseling Expertise in Online Support Forums
A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive.
Exploring the Value of Personalized Word Embeddings
Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4. 7% relative reduction in perplexity.
Compositional Demographic Word Embeddings
Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations.
Improving Low Compute Language Modeling with In-Domain Embedding Initialisation
In the process, we show that the standard convention of tying input and output embeddings does not improve perplexity when initializing with embeddings trained on in-domain data.
Expressive Interviewing: A Conversational System for Coping with COVID-19
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability.
Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog
We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners.
Targeted Sentiment to Understand Student Comments
We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments.
