Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues

  title={Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues},
  author={Sungjoon Park and Donghyun Kim and Alice H. Oh},
  booktitle={North American Chapter of the Association for Computational Linguistics},
The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. With proper anonymization, we collect counselor-client dialogues, define meaningful categories of client utterances with professional counselors, and develop… 

Figures and Tables from this paper

Automated Utterance Labeling of Conversations Using Natural Language Processing

This study explored how automated labels generated by NLP methods are comparable to human labels in the context of conversations on adulthood transition and proposed strategies to handle three common challenges raised in psychological studies.

Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

A method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems is proposed based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature.

Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards

This work develops an unsupervised methodology to quantify how counselors manage this balance between balancing between two key objectives: advancing the conversation towards a resolution, and empathetically addressing the crisis situation.

A Corpus of Simulated Counselling Sessions with Dialog Act Annotation

We present a corpus of simulated counselling sessions consisting of speech- and text-based dialogs in Cantonese. Consisting of 152K Chinese characters, the corpus labels the dialog act of both client

Towards Automated Real-time Evaluation in Text-based Counseling

An on009 line counseling platform is built, which allows profesional psychotherapists to provide free coun011 seling services to those are in need and labels the dataset using both coarseand fine-grained labels and uses a set of pretraining techniques.

Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling

. Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabilities (IDD) have more challenges than

Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context

A counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills is introduced and it is shown that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations.



Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

The recently proposed hierarchical recurrent encoder-decoder neural network is extended to the dialogue domain, and it is demonstrated that this model is competitive with state-of-the-art neural language models and back-off n-gram models.

Real-time Topic Models for Crisis Counseling

Fathom, a natural language interface powered by topic models to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging, is presented.

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

A neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps, that improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state is proposed.

A Hierarchical Latent Structure for Variational Conversation Modeling

A novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of using a hierarchical structure of latent variables, and exploiting an utterance drop regularization is proposed, which successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation.

Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

A large-scale, quantitative study on the discourse of text-message-based counseling conversations is presented and a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes are developed.

Regularizing and Optimizing LSTM Language Models

This paper proposes the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent regularization and introduces NT-ASGD, a variant of the averaged stochastic gradient method, wherein the averaging trigger is determined using a non-monotonic condition as opposed to being tuned by the user.

Linguistic Indicators of Severity and Progress in Online Text-based Therapy for Depression

An initial investigation into the application of computational linguistic techniques, such as topic and sentiment modelling, to online therapy for depression and anxiety finds that important measures can be predicted with comparable accuracy to face-to-face data, but measures of patient progress are captured only by finergrained lexical features.

Mining Themes and Interests in the Asperger’s and Autism Community

A novel model for web forums is presented, which captures both thematic content as well as user-specific interests and identifies several topics of concern to individuals who report being on the autism spectrum.

Universal Language Model Fine-tuning for Text Classification

This work proposes Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduces techniques that are key for fine- Tuning a language model.

Computational psychotherapy research: scaling up the evaluation of patient-provider interactions.

The utility of statistical text analysis models called topic models for discovering the underlying linguistic structure in psychotherapy is demonstrated, and predictive modeling demonstrated that topic model-derived features can classify therapy type with a high degree of accuracy.