8,425 research outputs found
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first introduce a Profile Model which encodes user profiles into
distributed embeddings and refers to conversation history from other similar
users. Then a Preference Model captures user preferences over knowledge base
entities to handle the ambiguity in user requests. The two models are combined
into the Personalized MemN2N. Experiments show that the proposed model achieves
qualitative performance improvements over state-of-the-art methods. As for
human evaluation, it also outperforms other approaches in terms of task
completion rate and user satisfaction.Comment: Accepted by AAAI 201
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions
to improve dialogue consistency. Collecting such descriptions from existing
dialogues is expensive and requires hand-crafted feature designs. In this
paper, we propose to extend Model-Agnostic Meta-Learning (MAML)(Finn et al.,
2017) to personalized dialogue learning without using any persona descriptions.
Our model learns to quickly adapt to new personas by leveraging only a few
dialogue samples collected from the same user, which is fundamentally different
from conditioning the response on the persona descriptions. Empirical results
on Persona-chat dataset (Zhang et al., 2018) indicate that our solution
outperforms non-meta-learning baselines using automatic evaluation metrics, and
in terms of human-evaluated fluency and consistency.Comment: Accepted in ACL 2019. Zhaojiang Lin* and Andrea Madotto* contributed
equally to this wor
Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function
Task-oriented dialog systems enable users to accomplish tasks using natural
language. State-of-the-art systems respond to users in the same way regardless
of their personalities, although personalizing dialogues can lead to higher
levels of adoption and better user experiences. Building personalized dialog
systems is an important, yet challenging endeavor and only a handful of works
took on the challenge. Most existing works rely on supervised learning
approaches and require laborious and expensive labeled training data for each
user profile. Additionally, collecting and labeling data for each user profile
is virtually impossible. In this work, we propose a novel framework, P-ToD, to
personalize task-oriented dialog systems capable of adapting to a wide range of
user profiles in an unsupervised fashion using a zero-shot generalizable reward
function. P-ToD uses a pre-trained GPT-2 as a backbone model and works in three
phases. Phase one performs task-specific training. Phase two kicks off
unsupervised personalization by leveraging the proximal policy optimization
algorithm that performs policy gradients guided by the zero-shot generalizable
reward function. Our novel reward function can quantify the quality of the
generated responses even for unseen profiles. The optional final phase
fine-tunes the personalized model using a few labeled training examples. We
conduct extensive experimental analysis using the personalized bAbI dialogue
benchmark for five tasks and up to 180 diverse user profiles. The experimental
results demonstrate that P-ToD, even when it had access to zero labeled
examples, outperforms state-of-the-art supervised personalization models and
achieves competitive performance on BLEU and ROUGE metrics when compared to a
strong fully-supervised GPT-2 baselineComment: 11 pages, 4 tables, 31st ACM International Conference on Information
and Knowledge Management (CIKM'22
- …