4 research outputs found
PAL: Persona-Augmented Emotional Support Conversation Generation
Due to the lack of human resources for mental health support, there is an
increasing demand for employing conversational agents for support. Recent work
has demonstrated the effectiveness of dialogue models in providing emotional
support. As previous studies have demonstrated that seekers' persona is an
important factor for effective support, we investigate whether there are
benefits to modeling such information in dialogue models for support. In this
paper, our empirical analysis verifies that persona has an important impact on
emotional support. Therefore, we propose a framework for dynamically inferring
and modeling seekers' persona. We first train a model for inferring the
seeker's persona from the conversation history. Accordingly, we propose PAL, a
model that leverages persona information and, in conjunction with our
strategy-based controllable generation method, provides personalized emotional
support. Automatic and manual evaluations demonstrate that PAL achieves
state-of-the-art results, outperforming the baselines on the studied benchmark.
Our code and data are publicly available at https://github.com/chengjl19/PAL.Comment: Accepted to ACL 2023 finding
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
We propose task-adaptive tokenization as a way to adapt the generation
pipeline to the specifics of a downstream task and enhance long-form generation
in mental health. Inspired by insights from cognitive science, our
task-adaptive tokenizer samples variable segmentations from multiple outcomes,
with sampling probabilities optimized based on task-specific data. We introduce
a strategy for building a specialized vocabulary and introduce a vocabulary
merging protocol that allows for the integration of task-specific tokens into
the pre-trained model's tokenization step. Through extensive experiments on
psychological question-answering tasks in both Chinese and English, we find
that our task-adaptive tokenization approach brings a significant improvement
in generation performance while using up to 60% fewer tokens. Preliminary
experiments point to promising results when using our tokenization approach
with very large language models.Comment: Accepted at the main conference of The 2023 Conference on Empirical
Methods in Natural Language Processing; 8 page
CEM: Commonsense-Aware Empathetic Response Generation
A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user’s emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses. Our code is available at https://github.com/Sahandfer/CEM
AugESC: Large-scale Data Augmentation for Emotional Support Conversation with Pre-trained Language Models
Crowd-sourcing is commonly adopted for dialog data collection. However, it is
highly costly and time-consuming, and the collected data is limited in scale
and topic coverage. In this paper, aiming to generate emotional support
conversations, we propose exploiting large-scale pre-trained language models
for data augmentation, and provide key findings in our pilot exploration. Our
adopted approach leverages the 6B-parameter GPT-J model and utilizes publicly
available dialog posts to trigger conversations on various topics. Then we
construct AugESC, a machine-augmented dataset for emotional support
conversation. It is two orders of magnitude larger than the original ESConv
dataset in scale, covers more diverse topics, and is shown to be of high
quality by human evaluation. Lastly, we demonstrate with interactive evaluation
that AugESC can further enhance dialog models tuned on ESConv to handle various
conversation topics and to provide significantly more effective emotional
support.Comment: Work in progres